A Multiple Description Video Codec With Adaptive Residual Distributed Coding

A Multiple Description Video Codec With Adaptive Residual Distributed Coding Jiann-Jone Chen,Member,IEEE,Shih-Chieh Lee,Ching-Hua Chen,Chen-Hsiang Sun,

Jyun-Jie Jhuang,and Chi-Chun Lu

Abstract—Multiple description coding(MDC)decomposes one single media into several descriptions and transmits them over different channels for error resilience.Each description con-tributes to improving the reconstructed media quality when decoded.Distributed video coding(DVC)encodes multiple cor-related images and utilizes error correction codes to shift the codec complexity to a joint https://www.doczj.com/doc/2e499645.html,bining MDC with DVC (MDVC)yields a stable codec for mobile encoders.In this paper, to improve the MDVC codec performance,image correlations among the MDVC processing modules were exploited to improve reconstructed video quality and enhance transmission robustness. At the side encoder,a DVC-based adaptive differential pulse code modulation was designed to remove interframe redundancy to enhance rate-distortion performances.For the MDVC central decoding,intradescription and interdescription correlations were utilized to dynamically select the best reconstructed frames from two descriptions,instead of selecting just one description or all key-frames from two descriptions.Experiments showed that,as compared to previous methods,the proposed MDVC control method yielded1–2dB higher in image PSNRs for Wyner–Ziv reconstructed frames at the side decoder when encoding low-to-medium complexity videos.For high-complexity videos,it effectively prevents error correction of Wyner–Ziv frames from malfunctioning and yields about3dB higher in PSNR.The proposed MDVC central decoder control yields1–4dB higher PSNRs,as compared to side decoders.Under lossy transmission, it demonstrates27–64%smaller PSNR variations,as compared to that of combining key-frames as the decoded video.The proposed MDVC system and control not only improve the DVC reconstructed video quality,but also reduce the quality ?uctuation artifacts of MDC coded video for mobile coders.

Index Terms—Adaptive central decoder control,multiple description video coding,residual distributed video coding, Wyner–Ziv video coder.

I.Introduction

T HE LATEST H.264/A VC video codec aims at a high compression ratio,based on which scalable video coding (SVC)has been developed as an extension.They exploit the interframe correlation by performing motion estimation and Manuscript received September22,2010;revised February1,2011and June29,2011;accepted October20,2011.Date of publication December12, 2011;date of current version May1,2012.This work was supported in part by the National Science Council,Taiwan,under Grant NSC100-2221-E011-156. This paper was recommended by Associate Editor E.Magli.

The authors are with the Department of Electrical Engineering, National Taiwan University of Science and Technology,Taipei106, Taiwan(e-mail:jjchen@https://www.doczj.com/doc/2e499645.html,.tw;d9307301@https://www.doczj.com/doc/2e499645.html,.tw; m9707312@https://www.doczj.com/doc/2e499645.html,.tw;m9507304@https://www.doczj.com/doc/2e499645.html,.tw;m9607318@ https://www.doczj.com/doc/2e499645.html,.tw;m9807313@https://www.doczj.com/doc/2e499645.html,.tw).

Color versions of one or more of the?gures in this paper are available online at https://www.doczj.com/doc/2e499645.html,.

Digital Object Identi?er10.1109/TCSVT.2011.2179459motion compensation to improve the rate-distortion coding performance.As a result,the encoder is more complex than the decoder,which is not suitable for applications like wireless video sensor networks,wireless video communications,and any codec system with processing power limitations.They require a different codec design compared to current video delivery systems and a key requirement is to transfer the en-coding complexity to the decoder.The distributed video coder (DVC)[1]was proposed to meet this requirement,and was developed based on lossless distributed source coding,also known as the Slepian–Wolf coder(SWC)[2].An important aspect of the SWC is that separated encoders can achieve higher compression ratio by exploiting the correlations among data streams through the joint decoder.This framework was extended to process lossy compression with side information (SI)at the decoder[3],as in the case of the Wyner–Ziv(WZ) coder,with which the DVC treats video compression as a channel coding problem.Images in a video are independently coded by error correction codes and only part of the parity bits are transmitted to the decoder.The decoder utilizes motion compensated interpolation/extrapolation to generate the SI and corrects errors based on the reconstructed systematic bits, SI,and parity bits[3].The traditional balance of a complex encoder and a simple decoder is essentially reversed.The DVC can also be applied to error resilience control[4] which treats the SI as additional reference information to correct transmission errors.To improve the DVC,multiple sets of SI[5]are used to reconstruct the original image for better PSNR performance.Another adopted super-resolution algorithm to upsample the nonkeyframe[6],in which a con?-dence value is used to scale the amount of high-frequency contents.The DISCOVER codec[7]adopted bidirectional motion compensated interpolation(BMI)to perform motion re?nement,spatial motion smoothing,and adaptive group of pictures(GOP)size control to improve the SI con?dence. To combat channel noise,multiple description coding (MDC)was proposed for robust transmission[8].The MDC is derived from information theory[9]and provides error resilience for signal streams.It is designed in a way that an arbitrary subset of descriptions can be used to decode the original stream[10],such that network congestion and packet loss would cause only a loss in quality instead of interrupting the stream.In general,the original signal I would be decomposed into several equally important subsets,say two descriptions,S1and S2,as illustrated in Fig.1.When both S1

1051-8215/$26.00c 2011IEEE

and S2are correctly received,the central decoder,D c,can best

reconstruct the signal.In other cases,the side decoder uses

correctly received description subsets,i.e.,d i∈{S1∪S2}, to reconstruct the signal with acceptable quality.For MDC,

an adaptive directional lifting transform[11]is used to de-

correlate samples on a quincunx lattice to achieve good

coding performance.For error robustness,a hybrid multiple

description codec[12]segments one video in both spatial

and frequency domains,such that its decoder can utilize

both residual-pixel correlations in the spatial domain and

coef?cient correlations in the frequency domain.To improve

coding ef?ciency,predictive coding and mismatch control can

be adopted for a MDC system[8],[14].More decomposed

descriptions lead to more redundant penalty in transmission

and more MDC decoding complexity penalty,e.g.,forward

error correction codes and joint decoding in D c,respectively.

The DVC,whose decoder requests only parity bits for a

WZ frame instead of redundant information to reconstruct the

original image frame,can be adopted by the MDC to eliminate

the redundant penalty.

The MDC assures stable and reliable communications with

multiple transmission paths and the DVC robustness can be

enhanced by the MDC to achieve comparable rate-distortion

performance.To integrate MDC with DVC[14],abbreviated

as MDVC,it was proposed that the MDC decoding can be

formulated as a WZ decoding problem.The drift on packet

loss in a MDC can be prevented by adopting hash codes

to improve the SWC ef?ciency[13].The source is usually

decomposed into several descriptions S i s,whose correlations

are encoded by the WZ encoder.When attacked by channel

noises,the lossy description?S i=S i can be recovered by the WZ decoder with the correlation information and the SI from other lossy descriptions.This WZ joint decoder can maintain acceptable reconstruction quality,in which WZ frames serve as extra coarser descriptions of one video and would be redundant when there is no transmission error[4].For one MDC example[15],the original video is decomposed into two descriptions and each description contains both Key and WZ frames,both of which are DWT transformed and quantized by multiple description scalar quantizer(MDSQ)for transmission robustness.The key and WZ frames are encoded by JPEG2000 and low-density parity-check,respectively,after MDSQ.The design target is to maintain acceptable reconstructed video quality when only one description was received,in which the cross-decoding of WZ frames would request fewer parity bits when received high-correlated descriptions.The MDVC can adopt transform domain WZ codec[16],in which a discrete cosine transform(DCT)procedure,T,is carried out before the quantizer,Q,which is denoted as MDVC-T.

One can also employ the residual2D-DVC[17]to remove

the redundancy of WZ bitstreams.To improve the MDVC

reconstruction quality at the subdecoder D i,we extended the

2D-DVC approach and proposed to exploit interframe corre-

lations among MDVC processing modules and encoded them

as WZ frames.This MDVC residual image coding is denoted

as MDVC-RT.A hash-based MDVC[13]exploits interframe

correlation and sends it with robust hash codewords,which

acts as the counterpart of the residual signal in MDVC-RT,

By Fig.1.MDVC system diagram.

performing differential pulse code modulation(DPCM),the rate-distortion performance and SI con?dence can be improved simultaneously to enhance the MDVC.We analyzed the error distribution along the MDVC codec paths to justify the DPCM prediction gain and improved SI con?dence,which contributed to the overall PSNR performances.When the MDVC central decoder receives and decodes all video descriptors,it would yield the best reconstructed quality by selecting key-frames from all subdecoders as the output video.Otherwise,it controls a description selection switch to select the subdecoder output video that demonstrates good quality[8].We proposed to improve this description-level switch control by controlling the switch at the frame-level according to the available in-formation provided by decoded images.At the decoder D, intradescription and interdescription image correlations were exploited by the central decoder to dynamically select the best reconstructed image for the output video.By adopting DPCM and intelligently controlling the switch at frame-level, the MDVC codec demonstrates higher and more stable PSNRs, as compared to previous approaches.

The rest of this paper is organized as follows.The MDVC system is reviewed in Section II.The MDVC system with predictive coding and residual WZ coding is presented in Section III.The frame-based dynamic central decoder control method is described in Section IV.The coding error distribution and SI con?dence analysis are provided in Section VII.Section VI is the simulation study.Section VII concludes this paper.

II.MDVC System

An MDVC codec framework with two descriptions is shown in Fig.1.At the encoder E,the input videoυin={I i}is demultiplexed to odd and even subsequences,{I2i?1}and{I2i}, which would be compressed by the SVC at side encoders E1 and E2,separately,to provide the key-frame bitstreams[16], denoted as K o and K e,respectively.In addition to an SVC, each side encoder E i also contains one WZ coder,as shown in Fig.2(a).The even subsequence{I2i}was sent to the side encoder E1for the WZ coder to encode the residual signal r E2i as the WZ frame bitstream,W e,and vice versa for W o of{I2i?1}.With separate key-frame and residual WZ frame bitstreams,the two descriptions for v in can be represented as S1=K o∪W e and S2=K e∪W o,respectively,as shown in Fig.2.The WZ coded residual image,W e or W o,helps to improve the temporal resolution of reconstructed video when the decoder does not receive all descriptions{S i}.The MDVC decoder,D,consists of two side decoders,D1and D2,

Fig.2.MDVC with a MDC of temporal decomposition:the codec diagram with control switches.(a)Encoder E.(b)Decoder D.

and one central decoder D c,as shown in Fig.2(b).The D1 takes S1as its input and the SVC decoder reconstructs?I D2i?1 from K o,which would provide the SI for the WZ decoder to

reconstruct even frames?I WZ

2i from W e.The other side decoder

would process S2in the same way.When both descriptions are received with tolerable errors,i.e.,?I D2i ?I E2i and?I D2i?1 ?I E2i?1, WZ frames would be discarded and the D c can provide the best reconstructed video,v rec={?I i}={?I D2i}∪{?I D2i?1}.When only one descriptor,say S1,was received with acceptable error,the corresponding side decoder can provide lower-quality video,i.e.,{?I i}={?I D2i?1}∪{?I WZ2i},which demonstrates images v rec={?I i}with?uctuating PSNR,as shown in Fig.13. In practical applications,the video descriptions S i may be transmitted through fading channels,which can be simulated by the Rayleigh fading channel model to allow evaluation of error robustness.For the MDVC system to facilitate operation under different coding control modes,one single-pole-three-throws and two single-pole-two-throws were installed to each side encoder E i and decoder D i,respectively,to serve as the mode switches,as shown in Fig.2.Setting all the switches to0signi?es the operation mode MDVC-T[16].The MDVC-RT method can be accomplished by turning all switches in Figs.2to1.Switch2is reserved to evaluate the performance of practical applications that provide low-complexity encoding while still implementing the MDVC-RT

mechanism.Fig.3.Wyner–Ziv residual coding of video.

III.MDVC-RT System

To describe the MDVC-RT operations,only the S1=K o∪W e processing needs to be presented since the counter-part operations in S2are carried out in the same way.By utilizing DPCM in the MDVC system,the WZ encoder processes block DCT coef?cients of the residual signals r E2i,which is the prediction error of{I2i}from the reconstructed odd subsequence{?I E2i?1},that is

r E2i=I2i??I P(E)

2i

=I2i?P({?I E2i?1})(1)

where P denotes the predictor function and?I P(E)

2i

denotes the predicted frame for I2i at E.The W e is generated from r E2i with the following three processing steps:

(a)c E2i=T[r E2i],(b)?c E2i=Q[c E2i],(c)W e=C[?c E2i](2) where Q,T,and C denote quantization,DCT,and channel coding with a turbo encoder,respectively.The former two operations were conducted in a block-wise approach,while the latter in block bit-plane coding.

A.Wyner–Ziv Residual Coding

The DVC framework in the MDVC system that adopts residual coding is shown in Fig.3,which performs predictive coding at the DVC encoder.The residual image,r E2i,to be encoded by the WZ coder,is the prediction error between

the original frame I2i and the predictive frame,?I P(E)

2i

.The ?I P(E)

2i

is selected from reconstructed frames of the SVC codec, which can be referenced at both the encoder and the decoder. The r E2i will be partitioned and transformed by a4×4block DCT,whose low-frequency coef?cients usually demonstrate larger variances and would be allocated more bits than high frequency ones.The numbers of quantization levels,2M k, for all coef?cients of a4×4DCT block[18]are shown in Fig.4(a),where M k and2M k denote the number of bits and quantization levels,respectively,for the k th transform coef?cient of PCM image signals.Three quantization patterns, {Q i}i=1,2,3,obtained through an optimal bit allocation proce-dure,Zonal Sampling[19],under different bit budgets,can be selected for quantization to provide scalable WZ frame bitstreams.To perform WZ residual image coding,since the signal variance and entropy of the residual image in the MDVC-RT would be smaller than that in the MDVC-T,the quantization patterns have to be re-designed.The same optimal bit allocation procedure was performed based on coef?cient variances of residual images to yield the new quantization

Fig.4.Quantization patterns for(a)MDVC-T and(b)MDVC-RT,where the numbers inside each pattern denote the number of quantization levels for that coef?cient and the allocated bits for one4×4block are10(Q1),17(Q2) and30(Q3),respectively.

patterns.In practical implementations,the quantization step-size is setup by adopting the loading factorγ=3.5[20]. Quantization patterns for the MDVC-RT are shown in Fig.4(b) and are denoted as RQ1,RQ2,and RQ3.In comparison,the RQ i allocate more bits for high frequency coef?cients than Q i in that the DPCM signals comprise more high frequency components.Similar to the quantization matrix used in JPEG and MPEG,the number of quantization levels speci?ed in Fig.4are estimated in advance and would be kept?xed for all its consequential coding operations.They are considered as default codec parameters,which only need to be transmitted at the initial codec and transmission negotiation stage.After quantization,each coef?cient,say k,in the4×4block is represented by its quantization index?c E2i(k).The quantized DCT block?c E2i is decomposed into bit-planes for the turbo coder.For each bit-plane,only the parity check bits are transmitted to the decoder,which would request more parity bits in the event that the turbo decoder cannot recover the data correctly.In addition,the skipped systematic bits at E are replaced by the reconstructed SI at D,which is obtained through the BMI procedure and residual signal estimations based on the SVC reconstructed images.

Rate compatible punctured turbo codes(RCPT)were intro-duced to provide unequal error protection on an unstable trans-mission channel[21].Automatic repeat request(ARQ)[22] was added into the architecture of alongside turbo codes, resulting in lower bitrate.Turbo coding[23]is applied to our proposed MDVC-RT system for channel coding.It has been shown to achieve performance close to the Shannon’s capacity limit in an additive white Gaussian noise channel with moderate decoding complexity[24].The RCPT exploits the turbo code framework and punctures a low-rate1/M code with period P periodically to obtain a family of codes.The rate of the codes is P/(P+l)where l is variable from1to (M?1)P.The concept was referred to earlier along with proposed frameworks.

In a DVC,the major encoding complexity,motion estima-tion(ME),is shifted to the decoder that acts as a hub to receive descriptions from light encoders and take care of the whole codec computations for further distribution[1].For light encoding,the keyframes are intracoded to eliminate the ME process.When DVC is integrated with MDC,the keyframes are usually encoded with SVC,such as H.264,motion-JP2, or MC-EZBC,for controlling bitrates and video quality[16]. Since MVs may not be available at D and the received descrip-tions,?S i,may present lossy coded video,the MDVC decoder is thus designed to perform ME based on reconstructed

images,Fig.5.Method to yield the interpolated frames?I int.(a)Bidirectional motion compensated prediction.(b)Reconstructed keyframe image?I D2i(left)and the interpolated one?I int2i(right).

?I D

2i±1

,for the BMI to yield SI.When MVs are available

from the received descriptions,the decoder can extract them without performing ME.Whether the actual MVs can yield a better interpolated image(SI)from low-bitrate-coded or noise attacked keyframes,compared to re-estimate MVs at decoder, is beyond the scope discussed in this research.However, adopting different MVs would improve/degrade the average PSNRs for all control methods in our work.To reconstruct

the WZ frame?I WZ

2i

,the system selected the corresponding key frames?I D2i±1={?I D2i?1,?I D2i+1}to perform ME for the BMI[25]to yield the reconstructed original image?I int2i.As shown in Fig.5(a),for the(m,n)th block,b2i(m,n),to be interpolated in?I int2i,the motion vector of the corresponding block in?I D2i+1, v(m,n)=(v x,v y),is utilized.All pixel graylevels in b2i(m,n)={?I2i(mB+j,nB+k),0≤j,k

?I

2i

(mB+j,nB+k)=?I2i?1(mB+

v x

2

+j,nB+

v y

2

+k) +?I2i+1(mB?

v x

2

+j,nB?

v y

2

+k),?0≤j,k

At E1,the predictive frame?I P(E)

2i

is selected from the SVC reconstructed frames,?I E2i?1.The residual WZ frame is obtained

by r E2i=I2i??I P(E)

2i

.At D1,the original frame I2i is not available and is replaced by the?I int2i through BMI.The estimated residual

image at D1is obtained by r SI2i=?I int2i??I P(D)

2i

.By performing procedures T and Q on the r SI2i,the turbo decoding function with its SI parameters can be represented as

{?c D2i}={C?1[W e]|SI=?c SI2i}(4) where C?1and SI denote the turbo decoding function and its side information parameter,respectively,and?c SI2i=Q[c SI2i]= Q[T[r SI2i]](see Fig.3).Note that the Q,T,channel coding, and the turbo codec in Fig.4serve as coding/transmission and error correction purposes,respectively,which are not necessarily the only ful?llment of a MDVC.When the turbo decoder recovers the quantization index?c D2i by(4),the re-construction module at WZ decoder(Fig.3)performs Q?1 and T?1to yield the preliminary reconstructed residual signal, i.e.,r D2i=T?1[c D2i]=T?1[Q?1[?c D2i]].The?nal reconstructed

Fig.6.

WZ codec reconstruction function:?r D 2i =RF (r SI 2i

,r D 2i ).residual signal is obtained [26]by the reconstruction function,

?r D 2i =RF (r SI 2i ,r D 2i ),shown in Fig.6,which assumes a 2

M k

=4level quantizer for simplicity.When the parameter r SI

2i resides

on the linear reconstruction interval of ?r D

2i ,i.e.,the interval

under the function slope =1,the ?r D

2i will take the value of r SI 2i ,e.g.,?r D 2i =r SI 2i =RF (r SI 2i ∈[0,···,63],r D 2i =0).When r SI 2i

resides on the saturated interval (function slope =0),the ?r D

2i will be replaced by the maximum value of the quantization bin,

e.g.,RF (r SI 2i >63,r D 2i =0)will set ?r D

2i =64.This function will

prevent the reconstructed ?r D

2i from deviating from the original

value too much because of SI errors.At the last stage,?I P (D )2i

will be error compensated by ?r D

2i

to yield the ?nal reconstructed image ?I WZ 2i .

B.Image Prediction of MDVC-RT

In performing DPCM,the side encoder has to exploit

temporal correlation between adjacent images to reduce the entropy of signals to be encoded.For one image I 2i to be

encoded,it has to ?nd the best predictive image ?I P (E )2i

to yield the best rate-distortion ef?ciency.For DPCM,the predictive

image,?I P (E )2i ,is selected from combinations of ?I E 2i ?1s such that

both E and D can refer to the same reconstructed image,i.e.,?I D 2i ?1 ?I E 2i ?1.In our experiments,the best ?I P (E )2i

are selected from three reconstructed images,i.e.,?I E 2i ?1,?I E 2i +1and ?I E 2i ?1+?I E 2i +12

.The procedure to select the best predictive image is described as follows:

1)calculate 0=|I 2i ?p 0(I 2i )|, 1=|I 2i ?p 1(I 2i )|and 2=|I 2i ?p 2(I 2i )|,where p 0,p 1,and p 2denote ?I E 2i ?1+?I E 2i +12

,?I E 2i ?1,and ?I E 2i +1,respectively;2)if 1≤ 1or 2≤ 2,then select the prediction mode p i that yields i =min { 1, 2};

3)if 1> 1and 2> 2and | 1? 2|≤ 0,then select prediction mode p 0;

4)if 1> 1and 2> 2and | 1? 2|> 0,then select prediction mode p i that yield i =min { 1, 2};5)repeat step 2to step 4for all images.

At our earliest setup,?xed prediction was adopted,i.e.,

either ?I E 2i ?1or ?I E 2i +1was used as the predictive image,?I P (E )2i ,

to yield the r E

2i .It cannot yield good prediction for medium to high motion videos.For the second setup,the image that

yields min { 1, 2}was selected as ?I P (E )2i .When 1≤ 1or

2≤ 2,it selected the mode that yields i =min { 1, 2}.This control step would cover the condition 1> 1.How-

ever,it also demonstrated inef?ciency for high-motion videos,which was improved by adopting the third prediction mode,

p 0(I 2i )=?I

E 2i ?1+?I E 2i +12,to exploit both forward and backward interframe correlations [30].The interframe distance i should be proportional to the video complexity during that period.When 1≤ 1and 2≤ 2,the video should be stable during this interval and either p 1or p 2would be adopted since the correlation between consequential images is high.When both 1and 2are larger than the threshold 1,and if | 1? 2|is small,the video should be in a high-motion period while still presenting near equal interframe correlations.If | 1? 2|is large,the correlation among these three images is small and

the system selects the reconstructed image ?I E 2i ±1that yielded

the minimum i .

The three thresholds,{ i }i =0,1,2={c 0·σ| 1? 2|,c 1·σ 1,c 2·σ 2},are set to be c 0=1,c 1=2,and c 2=2,respectively,where σx denotes the standard variation of x ,in which σ are estimated dynamically based on all the past i values.The scale factors,c i ,are set from experiments that can yield reasonable average DPCM prediction gain [20].If c 1or c 2was set smaller,the control step 3would be triggered often,which required additional average and division numerical operations.However,adopting larger c 1or c 2,in addition to reducing encoder complexity,can also provide good enough predicted frames such that WZ decoding can perform well.By setting {c 0,c 1,c 2}={1,2,2}from experiments,a good compromise can be achieved between the encoding complexity and decoder performance when dealing videos with different complexities.In a practical MDC system,the S i s are usually subjected to different probabilities of noise attack during transmission and

the reconstructed ?I D 2i ?1may be degraded due to incomplete

descriptions,S i =?S i .For D 1to reconstruct the ?I WZ 2i ,the

system has to perform interpolation from ?I D 2i ±1to provide SI

for the WZ decoder,such that ?I WZ 2i may contain errors from

both ?I int 2i and reconstructed residual images ?r D 2i (see Fig.3).

For example,when D 1received a severely degraded ?S

1,the reconstructed quality of ?I WZ 2i ?1at D 2may be higher than ?I D 2i ?1

at D 1.With an unpredictable transmission error,the D c has

to fully utilize all the available incomplete descriptions,?S

1,to yield the best reconstructed video.The center decoder control policy is described in the next section.

IV .Central Decoder Control

For a MDVC [8]with two descriptions,its D c can recon-struct the video with high quality when both descriptions,S 1and S 2,are received with correctable errors,i.e.,the

video {?I

key i }is selected by the sequence selector in Fig.7(a).When either one description was received correctly,say S 1,it

would provide the reconstructed video {?I D 2i ?1,?I WZ 2i }decoded

from ?S

1at D 1,i.e.,selecting ?I o i in Fig.7(a).Under this condition,the image PSNR would ?uctuate and demonstrate unpleasant visual quality.For practical implementations,the S i may be subject to time-varying transmission errors.These noise-attacked descriptions prevent the D c from reconstructing high quality videos.In other words,the D c (S 1,S 2)has to

dynamically select an image,either ?I

WZ i or ?I D i ,with higher reconstructed quality for ?I

i ,instead of just combining two key-

Fig.7.Central decoder control diagram for:(a)general MDC central decoder,and (b)proposed MDC central decoder with adaptive key-frame selection mechanism.The frame rate of the MUX input is f /2and the MUX operation frequency is f .The operation periods for the sequence and frame selector are N/f and 1/f ,

respectively.

Fig.8.Estimated intradescription and interdescription correlations used for

the proposed central decoder control algorithm.

frame sequences or selecting the output images exclusively from one of the two D i with correct S i .Fig.7(b)shows that the

frame selector would select either ?I

o i or ?I e i with the operation frequency f .

For one description,say S 1=K o ∪W e ,the SVC bitstream,K o ,is not error-robust while the WZ frames,W e ,are turbo encoded and where channel coding capability allows a certain degree of noise attack.When these substreams are transmitted

through noise channels,the ?I WZ 2i may demonstrate better error-robustness and reconstructed quality than ?I key 2i .At D 1,the

?I

D 2i ±1are utilized to provide the SI for the WZ frames (i.e.,parity bits)to reconstruct the ?I WZ 2i .This W e provides parity

information to correct errors between SI and the original

residual image,i.e.,between ?I int 2i ??I P (D )2i and I 2i ??I P (E )2i

(see Fig.3).As shown,the SI is obtained through the BMI

procedure ?I int 2i =BMI (?I D 2i ?1,?I D 2i +1)at D 1,such that the ?I D 2i ?1

quality affects ?I WZ 2i .For S 1,let 1= ?I D 2i +1??I D 2i ?1 1denote the

vector 1-norm of the intradescription frame difference between

reconstructed odd frames.For S 2,let 2?and 2

+denote the vector 1-norm of difference between even frames,i.e., ?I D 2i ??I D 2i ?2 1and ?I D 2i ??I D 2i +2 1,respectively.For interdescrip-tion frame difference,let 12?and 12+denote ?I D 2i ??I D 2i ?1 1

and ?I D 2i ??I D 2i +1 1,respectively.The control steps are described as follows:

1)if ( 12?+ 12+)<( 2?+ 2+),then select ?I D 2i ;2)elseif ( 2?+ 2+)<2;· 1,then select ?I D 2i ;3)else select ?I WZ 2i .

The decision conditions and the selection policy are ana-lyzed as follows.

1)When the ?rst condition is satis?ed,it means that the interdescription frame difference is smaller than that of the intradescription.We can infer that the current key-frame,?I D 2i ,was not degraded by noise attack and its

quality should be better than ?I WZ 2i .Hence,the D c selects

?I D 2i as the best option.

2)If the ?rst condition fails,it means that the intradescrip-tion frame difference is smaller.We can infer that there

are degraded images among the three key-frames ?I D 2i ,

?I D 2i ?1,and ?I D 2i ?1.The second condition is added to clarify which image was degraded.When satis?ed,the degraded

image should be either ?I D 2i +1or ?I D 2i ?1,and ?I WZ 2i would be

affected by this degradation.The D c selects ?I D 2i as the

best.

3)When both the ?rst and second conditions fail,it means

that the current key image ?I D 2i was degraded.We thus

select ?I

WZ 2i as the best image.V .MDVC Error Distribution Analysis

The encoding performance of WZ frames in the MDVC-RT can be analyzed from two aspects:the turbo coder and

the DPCM prediction.For the former,one WZ frame can be recovered from its SI if con?dence is high enough [27].For the latter,the DPCM can effectively reduce the quantization error

under the same bit budget due to prediction gain,G p =σ2

I σ2r

>1.

To compare the WZ codec performance between MDVC-T and -RT,both quantization error and SI con?dences were investigated.The MDVC signal processing unit is set to be a 4×4DCT coef?cient block whose bit-planes are grouped to yield parity bits (WZ frames)for error correction.Let the SI con?dence for one block be measured as the inverse of the difference between a signal block bit-plane,b 4×4,and its

SI,b SI 4×4,and be represented as b E 4×4?b SI 4×4 ?1.In general,if the SI con?dence is high enough,the turbo coder can fully

recover b 4×4from b SI

4×4with the parity information b p 4×4within certain iterations.Since the transform process,T ,is lossless,the representation of the time domain image I 2i is equivalent to the transform domain coef?cients c 2i in the following analysis.The WZ frame reconstruction can be represented by the following equation (see Fig.3):

c D 2i =c E 2i + WZ =c E 2i + Q + SW

(5)

where Q =?c E 2i ?c E 2i and SW =?c D 2i ??c E

2i denote quantization

error and SWC error,respectively.For the SW codec,the SI con?dence for DPCM signals with no quantization is equal

to that of PCM,i.e., c E 2i ?c SI 2i ?1PCM = c E

2i ?c SI 2i ?1DPCM .After quantization under the same bit budget,the quantization stepsize of PCM would be much larger than DPCM due to

prediction gain,σ2

I σ2r

>1and a ?xed loading factor was set

for quantizer,such that coef?cients for PCM signals would have large probability to be quantized into the same level,

which leads to pcm SI ≤ dpcm SI ,where (d )pcm SI =?c E

2i ??c SI 2i .In other words,the SI con?dence pcm SI ?1

would be higher

than dpcm

SI ?1.When encoding low-to-medium complexity

Fig.9.Turbo coder performance comparisons between MDVC-RT and MDVC-T.

videos,the SI con?dences for both MDVC-T and MDVC-RT, SI ?1s,are high enough such that pcm

SW 0and dpcm

SW 0.As shown in the upper sub?gure of Fig.9,the SWC can recover WZ frames well from SI for Salesman and Foreman videos,and demonstrates near equal error correction performance,e.g.,on the scale of 10?3and 10?4for MSE.When encoding the high complexity video,Football ,the inter-polated frame,?I int 2i ,bears little resemblance to I 2i ,i.e.,the error

I 2i ??I int 2i became larger,due to low interframe correlation,

such that the MDVC-T would have large probabilities of encoding image blocks with low SI con?dences,under which the probability of error correction malfunction also increases,

i.e.,the SW decoder cannot fully recover ?c E

2i from ?c SI 2i within certain iterations and leads to pcm SW >> dpcm

SW .Under this

condition, dpcm SI is still a little larger than pcm

SI due to DPCM prediction gain and coarser quantization stepsize of PCM,as in the low-to-medium video case.However,the signal SI in MDVC-RT is uniformly small and can be corrected from

SI,i.e., dpcm

SW 0,while in MDVC-T,the number of large SI blocks increases which cannot be corrected from SI.In other words,the probability of processing low SI con?dence blocks is kept low in MDVC-RT,such that the SW codec performance would not be severely degraded as in MDVC-T.As shown in Fig.9,for Football ,the SW codec error dpcm SW is smaller than pcm

SW .For quantization,according

to (5),since pcm SW and dpcm

SW are very small for low-to-medium complexity videos,the ?nal reconstruction error,

WZ = c E 2i ?c D

2i ,would be dominated by Q .Experi-ments justi?ed that dpcm Q << pcm

Q ,due to G p >>1,which is measured to be 16–17,140–165,and 65–300for Football ,Foreman ,and Salesman ,respectively,under different transmission rates.The resultant WZ coding error in MSE, WZ 2,is demonstrated in Fig.10,which shows that the MDVC-T reconstructed WZ frames degrade with increasing video complexity,while the MDVC-RT ones degrade less.The above analysis justi?ed the reduction of WZ for MDVC-RT over MDVC-T becomes signi?cant when dealing with higher complexity videos.A simple error distribution analysis for the MDVC is illustrated in Appendix

A.

Fig.10.Wyner–Ziv codec performance comparisons between MDVC-RT and MDVC-T.

VI.Simulation Study

The MDVC-RT codec performance is investigated in this section.The single description coder (SDC)and MDVC-T are also implemented for comparisons.Three videos,Salesman ,Foreman ,and Football ,in QCIF at 15f/s that present low-,medium-,and high-complexity,respectively,are used as test videos.The GOP length is 2and the coded {I i }in S 1

and S 2can be represented as {I 2i ?1,I WZ 2i }and {I WZ

2i ?1,I 2i },respectively.The video complexity is measured quantitatively as the inverse of the interframe correlation coef?cient averaged for one video,and this ranks Football and Salesman as videos with the highest and smallest complexity,respectively.Different bit rate ranges are allocated for different sequences to exploit their reasonable rate-distortion operation range.The allocated bit rate (kb/s)ranges for the SVC are {80,100,150,300,600,900}for Salesman ,{80,150,250,400,600,1000}for Foreman ,and {100,200,300,400,500,600,900,1200}for Football .To control the bit rate of WZ frames,different quantization patterns discussed in Section III-A are used to provide different coded bit rates,i.e.,Q i and RQ i ,for MDVC-T and MDVC-RT,respectively.Either H.264or MC-EZBC [29]can be used as the SVC to encode key-frames,and the latter is adopted in considering practical scalability control.A.PSNR of Lossless Transmission D c (?S

1=S 1,?S 2=S 2)When both descriptions are received with recoverable errors

at D ,the D c combines the two key-frame sequences decoded

from SVC,{?I D 2i ?1}and {?I D 2i },by a multiplexer to provide the

reconstructed video ?I

i [see Fig.7(a)].As shown in Fig.11,the PSNRs of the reconstructed video from the central decoder ?I

i are very close to SDC but the WZ frames coded with a speci?c bit allocation pattern require additional bit rates,either for MDVC-RT or for MDVC-T.For comparisons,the SDC is labeled,which demonstrates the ideal reconstruction quality under a certain bit rate.The amount of additional bit rates

for both ?I

WZ (Q i )and ?I WZ (RQ i )is equal in that the same total bits are allocated to the WZ frames at the encoder.The reconstructed video can be represented as follows:

{?I i }={?I D 2i ?1}∪{?I D 2i }from D c (?S 1=S 1,?S 2=S 2).

(6)

Fig.11.Image PSNRs versus bit rate of(S1+S2)at the central decoder for different codecs:MDC,MDVC-T,and MDVC-RT.(a)Salesman.(b)Foreman.

(c)Football.

When both descriptions were received with recoverable errors, D c(?S1=S1,?S2=S2),it provides the best reconstructed video and the WZ frames cannot contribute to the quality of?I i.

B.PSNR of Single Description D c(?S1=S1,?S2=0)

When only one description,say S1,was received at the MDVC decoder,the D c takes the reconstructed frames from

both SVC and WZ decoders at the D1,i.e.,?I D2i?1and?I WZ

2i ,

as the?nal reconstructed video,which can be represented as follows:

{?I i}={?I D2i?1}∪{?I WZ2i}from D c(?S1=S1,?S2=0).(7) In MDC,missing frames at D1are obtained through BMI procedure based on I D2i?1,which leads to unpleasant visual quality and lower average PSNR.This artifact becomes

worse Fig.12.Image PSNRs versus bit rate of(S1+S2)at the side decoder for different codecs:MDC,MDVC-T and MDVC-RT,in which the PSNRs are averaged from both?I D2i?1and?I WZ

2i

.(a)Salesman.(b)Foreman.(c)Football.

when dealing with high-complexity videos,whose small in-terframe correlation yields inaccurate interpolated frames.In MDVC,the additional bitstream,W e,provides parity infor-mation for the WZ frames to be reconstructed to correct remaining errors between?c E2i and?c SI2i.The system can thus improve codec(E i/D i)performance in a MDC.At low bit rate coding,as shown in Fig.12,the MDC outperforms the MDVC in image PSNRs,both with the same average bit rate R b,in that the MDVC allocates several bits,R MDVC(W), for WZ frames such that the image?I D2i?1reconstructed with R b?R MDVC(W)bits cannot provide good enough interpolated image?I int2i and hence the SI,?c SI2i,for the WZ decoder to yield

good?I WZ

2i

.The average PSNR of images{?I D2i?1∪?I WZ2i}MDVC(R b) is thus smaller than that of{?I D2i?1∪?I int2i}MDC(R b).When high enough bits were allocated for the SVC coder to provide high quality?I D2i at D,the error correction capability of WZ images

Fig.13.PSNR variations for central and side decoders.(a)Salesman at 259kb/s(Q1).(b)Foreman at359kb/s(Q1).(c)Football at459kb/s(Q1). becomes signi?cant with the accurate SI which contributes

much toward improving the?I WZ

2i quality.As shown in Fig.12,

the MDVC outperforms MDC in image PSNRs when the total bit rate(in kb/s)exceeds around200(Salesman),300 (Foreman),and400(Football)for different complexity videos. As compared to previous work[15],the proposed MDVC-RT yields0.33dB PSNR improvement at a bit rate of300kb/s. The SDC performance is also provided for comparison. When coding the low complexity video,Salesman,the PSNR was improved by0.8dB and 1.2dB for MDVC-RT(RQ i),as compared with MDVC-T(Q i)for i=1and i=3,respectively.When encoding higher complexity videos, Foreman and Football,the amount of improved PSNR can be larger than2dB for Football(RQ1).As compared to the2D-DVC[17]that adopted the p0prediction mode,SW-SPHIT, and zero motion H.264,the proposed MDVC-RT yielded1dB higher PSNR for the Foreman sequence.As analyzed and

justi?ed in Section VII,in addition to the DPCM prediction

gain,the probability of low SI con?dence blocks can be kept

low by MDVC-RT,as compared with MDVC-T.These results

demonstrate that the MDVC performs better than MDC at the

cost of few extra bit rates for signal parity information.It is

well utilized by the proposed MDVC-RT to further improve

the PSNR performance of a MDVC side decoder.To justify

the capability of the proposed MDVC-RT method in coding

higher resolution videos,experiments on CIF format videos

have been conducted for veri?cation.The PSNR performances

for different videos under different bitrates and discussions are

provided in Fig.20in Appendix B.

Fig.13also shows that integrating MDC with DVC helps

to yield stable image PSNRs,as compared to the one without.

To achieve stable transmission through MDC and complexity

reduction through DVC,the MDVC system is operated at a

higher bit rate,as compared to one single traditional video

coder,MPEG-2or MPEG-4.For MDC,when both S1and S2were received by the D,the reconstructed video?I i at the D c demonstrated small PSNR variation.When the D received only one description,say S1,the missing images?I D2i s in S2

have to be interpolated from?I D2i?1s in S1,which yield severely

?uctuating{?I i}.The MDVC helps to stabilize the?uctuating ?I

i

,as shown in Fig.13.In addition,with the help of DPCM

prediction gain and improved SI con?dence,the MDVC-RT

yielded better reconstructed images,?I WZ

2i

,and demonstrated

smaller quality?uctuations for{?I i}={?I D2i?1,?I WZ2i},as com-pared to both MDVC and MDVC-T.

For subjective evaluation,the images reconstructed at the D c

with different control methods are provided for comparison.

Fig.14(a)demonstrates the original images.The ones recon-

structed by the MDC using BMI,MDVC-T and MDVC-RT

are displayed in Fig.14(b)–(d),respectively.These images are

reconstructed when the SVC coding bit rate is R(K)and the

WZ frame is allocated R MDVC(W)bits and quantized by the

Q1and RQ1patterns.For fairness,the bitrate of WZ frames,

R MDVC(W),was added to that of MDC w/o WZ coding,

R MDC(K),in the above evaluation.As shown,the image

obtained by BMI,?I int,suffers severe block artifact,especially

for the high-motion video Football.This is to be expected

since no corrective information is available for the missing

frames.With the help of the corrective parity information

for the?I WZ,the MDVC-T eliminates the block artifact to a

large extent,which can be further improved by the MDVC-

RT method.As shown,Fig.14(d)demonstrates much better

visual quality as compared with the others.

C.Transmission over Wireless Fading Channel(?S i=S i)

In practical video communication,the MDVC descriptions

may be transmitted through lossy wireless channels,?S i=S i. The Rayleigh fading channel model is used to simulate practical wireless transmission for the MDVC system.To transmit the WZ frame bitstream,the turbo code[23]is adopted for error correction.Utilizing the turbo code on the Rayleigh fading channel has been proven to approach the capacity limit on fully interleaved fading channels[24].To evaluate the error robustness of the proposed MDVC-RT,the

Fig.14.Subjective evaluation of the reconstructed images under the same total bitrate,R MDC (K )=R MDVC -T (K +W )=R MDVC -RT (K +W ).(a)Original

image,I 2i .(b)Reconstructed with the BMI procedure,?I int 2i

.(c)Reconstructed with MDVC-T,?I WZ 2i (Q ).(d)Reconstructed with MDVC-RT,?I WZ 2i

(RQ ).PSNRs of reconstructed images at D 1are compared among

different methods.When the S 1={K o ,W e }is attacked by

channel noises,the lossy K o would lead to degraded ?I D 2i ?1

while the lossy W e can be recovered by the turbo code whose correcting capability pushes the reconstructed frame

from ?c SI 2i toward the correct results,?c

D 2i ?c E

2i .As shown in Fig.15,the images obtained by BMI demonstrate the lowest PSNRs since there is no corrective information for the degraded images.With the error robust W e and the corrective capability of WZ-frames,the image PSNRs by MDVC-T are higher than those of BMI.The MDVC-RT further improves the rate-distortion performance.The MDVC codec performance can be analyzed from the viewpoints of channel noise and video complexity,in which the latter dominates.As mentioned above,the error robust W e /W o can combat channel noises for the MDVC-T/-RT when the SI con?dence measure SI s are smaller than a threshold value, T .From experiments, DCT SI

became larger when encoding higher complexity videos through fading channels.Under this condition,the probability of correcting function breakdown increases.On the contrary,the probability for RDCT SI

< T can be kept high due to DPCM prediction gain,under which the turbo coder keeps functioning well.As shown in Fig.15(b)and (c),the PSNRs were not severely degraded for images by the MDVC-RT as compared to the MDVC-T for medium and high complexity videos.The PSNR improvements of both MDVC-RT and MDVC-T under different BERs,in comparisons with the MDC-BMI,are listed in Table I,which shows that the MDVC-RT yields better PSNR improvements,especially for medium to high motion videos.On the other side,performance comparisons under fair setup conditions are required,such that all reconstruction methods are allocated equal bitrates,i.e.,R MDC (K )=R MDVC -T (K +W )=R MDVC -RT (K +W ).When coding low-complexity videos under lossy transmission,the MDC w/o WZ coding yielded 2–3dB higher PSNRs,compared to others.It is reasonable since the bitrate for keyframes,R MDC (K ),has been increased up to 50%,as compared to that of MDVC,R MDVC -T (K )and R MDVC -RT (K ).In addition,due to low-motion,the

interpolated

Fig.15.Image PSNRs,averaged from both ?I D 2i ?1

and ?I WZ 2i ,of reconstructed videos,transmitted through fading channels with different bit error rates.(a)Salesman at 159kb/s (Q 1).(b)Foreman at 209kb/s (Q 1).(c)Football at 459kb/s (Q 1).

TABLE I

Improvement in Average PSNRs by MDVC-T,MDVC-RT

Compared to MDC-BMI BER 10?210?310?410?510?6Scheme -T -RT -T -RT -T -RT -T -RT -T -RT Salesman 1.320.69 1.201.49 1.361.76 1.292.00 1.402.35Foreman 1.581.98 1.473.15 1.523.14 1.483.21 1.603.42Football

0.014.38

0.306.04

0.136.00

0.326.07

0.236.06

images would quite resemble to adjacent ones [the uppermost image in Fig.15(b)]and the allocated rates for MDVC,R MDVC -T (W )and R MDVC -RT (W ),not only decrease R MDVC (K )but also contribute less to improve the PSNR.For medium to high complexity videos,though the interpolated image,?I int 2i ,suffers severe block artifact [the lower two ?gures in Fig.15(b)],the WZ coding can effectively correct these errors

Fig.16.PSNRs of reconstructed frames of different central decoder control methods with BER(S2) 0and different BER(S1)at159kb/s.(a)Salesman with BER(S1)=10?5.(b)Salesman with BER(S1)=10?3.(c)Salesman with BER(S1)=10?2.

due to allocated R MDVC-T(W)and R MDVC-RT(W),as shown

in Fig.15(c)and(d).In comparisons,the contribution of

allocated R MDVC(W)in error correction is much larger than

the quality improvement due to increased R MDC(K).Under

this condition,the superiority of increasing R MDC(K)becomes

insigni?cant when coding higher-complexity videos.As shown

in Fig.16(b)and(c),the proposed MDVC-RT yielded higher

and more stable PSNR performances than the others.Although

the design of a MDC w/o WZ is simple and ef?cient in

coding low complexity videos,the MDVC-RT can work well

for medium to high complexity videos.

D.Central Decoder Control

When both descriptions are received with recoverable errors,

the central decoder,D c,takes key-frames from all D i s to yield

the high quality video,i.e.,{?I i}={?I D2i?1}∪{?I D2i}.This general MDC central decoder control is denoted as central-?xed.If any

descriptions were seriously attacked by channel noise,say S1,

selecting key-frames from all side decoders may not yield the

best reconstructed video{?I i}.Because the WZ frames?I WZ2i?1 reconstructed from S2may demonstrate higher image quality due to the turbo code error correction capability and

accurate Fig.17.PSNRs of reconstructed frames of different central decoder control methods with BER(S2) 0and different BER(S1)for Foreman at209kb/s and Football at459kb/s.(a)Foreman with BER(S1)=10?2.(b)Football with BER(S1)=10?2.

SI provided by?I D2i s.The proposed central decoder control method can effectively select a better reconstructed image, either from?I D2i or?I WZ

2i

,to yield one better reconstructed video, which is denoted as central-auto.

For the?rst experiment,let S2be received with recoverable errors and S1be attacked by channel noise with bit error rate BER(S1)=10?5,10?3and10?2,respectively.The image PSNRs by the central-?xed,central-auto,and both side decoders are presented in Fig.16.When BER(S1)<10?5, the?I D2i?1still demonstrates good image quality.Both central-?xed and central-auto select key-frames as the?nal best reconstructed video.When BER(S1)=10?5,some degraded ?I D

2i?1

s became worse than?I WZ

2i?1

s from S2.The central-auto can effectively select the one with best quality,as compared to central-?xed.As shown in Fig.16,when BER(S1)=10?5, the PSNR can be improved from32.65dB for central-?xed to32.79dB for central-auto.When BER(S1)=10?3and BER(S1)=10?2,the amount of improved PSNRs are0.45dB and2.12dB,whose PSNR variations are shown in Fig.16(b) and(c),respectively.The PSNR variation is also smaller for central-auto.When encoding higher complexity videos, Foreman and Football,with BER(S1)=10?2,the key-frame quality was severely degraded.The SI obtained through BMI bears low con?dence and hence the reconstructed WZ frames demonstrate inferior image quality.Under this condition,both central-?xed and central-auto selected nearly all key-frames as the best candidate images,as shown in Fig.17.

For the second experiment,to verify the robustness of central-auto,both descriptions are subjected to different noise attack probability with BER(S i)=10?5,10?4,and10?2,

Fig.18.PSNR of reconstructed frames of different central decoder control methods with different BER(S1)and BER(S2)at159kb/s.(a)Salesman with BER(S1)=10?2and BER(S2)=10?4.(b)Salesman with BER(S1)=10?5 and BER(S2)=10?5.

TABLE II

Comparisons of Average Image PSNRs Among MDVC-T,

SIMP,and MDVC-RT

Rate(kb/s)100200300600 Scheme-T SIMP-RT-T SIMP-RT-T SIMP-RT-T SIMP-RT Salesman32.832.833.336.737.137.339.039.839.843.244.144.1 Foreman29.530.530.832.333.433.533.834.634.836.437.437.4 Football21.122.023.525.827.827.927.029.129.129.831.731.8 respectively.Fig.18demonstrates that the central-auto yields higher average PSNR and smaller variation,as compared to those of central-?xed and both side decoders.

Note that some good quality images were not selected as the?nal output.

For example,some images with abrupt PSNR drop,e.g.,63,69,75,89,and105in Fig.16(b)and110, 119in Fig.18(b).Reasons for not being selected are as follows.

1)The central-auto method can only exploit the correlation

between key-frames at D,which are reconstructed from descriptions with different channel errors.This unpre-dictable quality degradation may destroy the original interframe correlation and thus biased the decision. 2)Only interframe correlations are utilized to make the

selection and no encoder signal properties,i.e.,the original video,can be referenced to decide whether reconstructed images are good or not.These constraints make it hard to make correct decisions in selecting good reconstructed images.Fig.19.Intuitive and simpli?ed MDVC-T/-RT signal error analysis diagram.

E.Practical Implementation

The DVC is designed to provide a low-complexity encoder for mobile devices to code and transmit videos.One additional decoder(SVC?1)and one prediction unit are required for the MDVC-RT to yield better coding performance,which con?icts with the DVC design target.To bene?t from the residual WZ frame coding,while satisfying the DVC design target, a simpli?ed MDVC(SIMP)was implemented to justify the proposed MDVC-RT method.As shown in Fig.2,when both switches are in position0/1/2,the codec system is operated in the MDVC-T/-RT/SIMP mode.Taking S1processing as an example,the SIMP mode takes I2i?1directly as the?I P(E)

2i to eliminate the additional prediction and decoder processing units,i.e.,r2i=I2i?1?I2i.This residual signal will be encoded as a WZ frame as that in the MDVC-RT.The image PSNR performances of MDVC-RT,MDVC-T,and SIMP are listed in Table II,showing that the SIMP outperforms MDVC-T and only degrades a little as compared with MDVC-RT.The results justify the proposal that MDVC-RT can be implemented on practical mobile devices to provide better and more stable video communications.

https://www.doczj.com/doc/2e499645.html,plexity Analysis

The time complexity overhead of MDVC-RT over MDVC-T is discussed in this section.Denote the number of pixels in one image as P=H·W.For SVC,the time complexity of MC-EZBC encoder is mainly from motion estimation and the num-ber of operations can be estimated as T(SVC E)=P·(2R+1)2 where R is the search range.The MC-EZBC decoder complex-ity[31]is estimated to be T(SVC D)=4·

J

j=0

(1

2

)j?1(operation

pixel

)·P(pixels)where J and j are the total number and level index of decompositions,respectively,and the maximum of T(SVC D)is8P.For DVC,the encoder performs prediction which requires P SUB operations for modes0and1to com-pute the difference between I2i,I2i?1,and2P SUBs/ADDs for mode2(worst case)to compute the average,e.g.,T(DVC E)= 2P.For DVC decoder,the block length is equal to the number of symbols,e.g.,total pixels in one image,P,and the required computations come from turbo decoding with k queues.The time complexity of DVC decoder is evaluated as

T(DVC D)=N p·I tc·2·P·T(TD)

where T(TD)=(8·22k+20·2k+1)[32],N p is the number of bitplanes,I tc is the iteration times,and

Fig.20.CIF image PSNRs versus bitrate of(S1+S2)at the side decoders of MDC,MDVC-T and-RT,whose PSNRs are averaged from both?I D2i?1and

?I WZ 2i .The rate comprises both keyframe(300k–1500k)and WZ frames bits

rates:Q1(237.6k),Q2(403.92k),Q3(712.8k).(a)Salesman.(b)Foreman.

k is the number of turbo decoder queues.The time complexity T(MDVC-T)and T(MDVC-RT)can be esti-mated as T(SVC E)+T(SVC D)+T(DVC D)and T(SVC E)+2·T(SVC D)+T(DVC E)+T(DVC D),respectively.The overhead of MDVC-RT over MDVC-T is computed to be

T MDVC-RT?T MDVC-T

T MDVC-RT =

T SVC

D

+T DVC

E

T SVC

E

+2·T SVC

D

+T DVC

E

+T DVC

D

where T(m)is represented as T m for simplicity.In our exper-iments,the parameters are N p=16,l tc=6,and k=4,which would get this overhead to be10

307+192·T(TD)

=0.0077%.

VII.Conclusion

For a multiple description coding system integrated with a distributed coder,MDVC,we proposed to exploit interframe correlations among processing modules of the MDVC to improve the codec performance.Contributions of this paper are described with regard to three aspects.

1)For the MDVC encoder,the DVC is designed to deal

with the residual image to improve the rate-distortion performance.The correlations are obtained by predictive coding which was developed from the viewpoint of interframe correlation to select the best predictive frame to maintain stable prediction results.The residual image signal was encoded by the WZ encoder to provide robust transmission.Simulations veri?ed that the resid-ual WZ coding achieves a reasonable prediction gain which helps to maintain high probabilities of encoding high SI con?dence codeblocks for the WZ coder.The reconstructed WZ frames by the MDVC-RT framework

demonstrated higher PSNRs,as compared with the one

without prediction.

2)At the MDVC decoder,an adaptive central decoder con-

trol method was proposed to select the best reconstructed

images,based on the interframe correlations between

key-frames at the decoder.Simulations showed that the

proposed method effectively selected good quality WZ

frames when the key-frames were severely degraded.As

compared with the non-adaptive ones,the reconstructed

video demonstrated higher average PSNR and lower

PSNR variations.

3)Keeping SI con?dence high through prediction gain is

the most distinguished feature of the proposed MDVC-

RT,which effectively exploited correlations among pro-

cessing modules.It helps to maintain stable recon-

structed video quality,especially for medium to high

complexity videos.Recent video coding applications,

such as interactive3-D TV or multiview video coding,

require extensive exploitation of interframe or inter-

video correlations to improve codec performance.The

proposed MDVC-RT mechanism can be extended to

provide an ef?cient codec platform for mobile encoders

of these applications and this is considered as our future

research.

A PPENDIX A

T URBO C ODER P ERFORMANCE

The error analysis diagram in Fig.19is provided,based on

experimental results and ideal signal processing assumption,to

help understanding the MDVC signal processing.To simplify

the signal analysis from the viewpoint of encoding I2i,all sig-

nal processing units in MDVC are classi?ed as either divergent

or convergent processes whose output signals deviated from

or convergent to I2i.The WZ frame reconstruction of I2i starts

from the decoded?I D2i?1.The?I int2i obtained from BMI working

on?I D2i±1would demonstrate a smaller error distance to I2i,i.e., ?I int2i?I2i < ?I D2i±1?I2i .The quantization,Q,is basically a noise process whose error under a certain bit budget,R,

is proportional to signal variances,i.e., Q 2 2σ2I2?2R. When encoding low-to-medium videos the SI con?dences are

high enough for both MDVC-T and-RT,such that the turbo

coder can correct the error SI = ?c E2i??c SI2i from?c SI2i to?c E2i. Under this condition,the Q process would dominate the?nal

WZ reconstruction error,i.e.,the pcm

Q

and dpcm

Q

in Fig.19. Due to DPCM prediction gain,σ2I>>σ2r,the?nal WZ frame reconstruction error of MDVC-T would be much larger than MDVC-RT,i.e., ?c E2i?c E2i PCM>> ?c E2i?c E2i DPCM.When encoding high-complexity videos by MDVC-T,in addition to the longer error distance, ?I int2i?I2i ,the turbo coder would have large probability to process blocks with low con?dence SI,due to enlarged SI.For MDVC-RT,both SW and Q would be larger when encoding higher complexity videos, in which the SI would also increase but the probability of processing blocks with low con?dence SI can be kept low and leads to quite small SW.The?nal WZ reconstruction error of MDVC-RT is still dominated by Q,which is smaller than that in MDVC-T due to DPCM prediction gain.An intuitive Q behavior modeling from experiments can be demonstrated with

the aides of Fig.19.Though error vectors dpcm Q (r SI )s and dpcm

Q (r E )s are random and

E [ dpcm

SI 2]

E [ pcm SI 2]

=12.8～1.2(in quantization index)

for low to high complexity videos, dpcm

SI s are uniformly small

and almost all can be corrected,while more pcm

SI s cannot be corrected when encoding higher complexity videos and result

in pcm SW >> dpcm SW .

A PPENDIX B

MDVC P ERFORMANCE ON CIF V IDEOS

Videos may demonstrate different pictorial details and mo-tion activity in QCIF and CIF formats.For CIF videos,Salesman is considered as a low-motion video,as in QCIF,while Football and Foreman can both be considered as high-motion videos.The rate versus PSNR plots of MDVC systems for different videos are shown in Fig.20.For Salesman ,the PSNR improvement is about 1–2dB for Q 2and Q 3.For high-motion videos,Foreman and Football ,the PSNR improvement can be up to 3–3.5dB for Q 1due to DPCM prediction gain.For Football ,the prediction gain was compensated by suf?-cient bit allocations,Q 2and Q 3,and MDVC-RT outperforms MDVC-T with comparable PSNRs.For Foreman ,due to high-motion,the PSNR improvement keeps becoming smaller from 3.5dB to 2dB for Q 1to Q https://www.doczj.com/doc/2e499645.html,paring QCIF with CIF video for MDVC processing,the key frames of the latter can provide higher SI con?dence due to higher resolution image descriptions,such that it can achieve PSNR improvement constantly without waiting for key-frames to be good enough to make WZ coding working,as in the case of encoding QCIF video.Further improvement is possible,in that the quantization pattern RQ i for different image resolutions may be re?ned,e.g.,8×8blocks,by the optimal bit allocation method,which is beyond the scope of present discussions.

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Jiann-Jone Chen(M’05)received the B.S.E.E.

and M.S.E.E.degrees from National Cheng-Kung

University,Tainan,Taiwan,in1989and1991,re-

spectively,and the Ph.D.E.E.degree from National

Chiao-Tung University,Hsinchu,Taiwan,in1997.

From1999to2002,he was with the ITRI Com-

puter and Communication Laboratories,Hsinchu.He

joined the Department of Electrical Engineering,

National Taiwan University of Science and Tech-

nology,Taipei,Taiwan,in August2002.He has

conducted research in video encoding,IPTV media

streaming,image indexing,and image/video processing.His current research

interests include various topics in digital image processing and multimedia

communications.

Dr.Chen received the ISI Classic Citation Award in

2001.

Shih-Chieh Lee received the B.S.E.E.and M.S.E.E.

degrees from Chung-Yuan Christian University,

TaoYuan,Taiwan,in1995and2003,respectively.He

is currently pursuing the Ph.D.E.E.degree with the

Department of Electrical Engineering,National Tai-

wan University of Science and Technology,Taipei,

Taiwan.

His current research interests include distributed

video coding,multiview video coding,video com-

pression,and multimedia

communications.

Ching-Hua Chen received the B.S.E.E.and

M.S.E.E.degrees from the National Kaohsiung Uni-

versity of Applied Sciences,Kaohsiung,Taiwan,

and the National Taiwan University of Science and

Technology,Taipei,Taiwan,in2008and2010,re-

spectively.

His current research interests include distributed

video coding,multiview video coding,video com-

pression,and multimedia

communications.

Chen-Hsiang Sun received the B.S.E.E.and

M.S.E.E.degrees from the National Taiwan Univer-

sity of Science and Technology,Taipei,Taiwan,in

2006and2008,respectively.

His current research interests include multiple de-

scription video coding,distributed video coding,and

multimedia

communications.

Jyun-Jie Jhuang received the B.S.E.E.and

M.S.E.E.degrees from the National Taiwan Univer-

sity of Science and Technology,Taipei,Taiwan,in

2007and2009,respectively.

His current research interests include image cod-

ing,distributed video coding,and video communi-

cations.

Chi-Chun Lu received the B.S.E.E.and M.S.E.E.

degrees from Fu-Jen Catholic University,New

Taipei,Taiwan,and the National Taiwan University

of Science and Technology,Taipei,Taiwan,in2009

and2011,respectively.

His current research interests include multiview

video coding,3-D video coding,and multimedia

communications.

域名解析教程

域名解析详细教程 域名解析是把域名指向网站空间IP，让人们通过注册的域名可以方便地访问到网站一种服务。域名解析也叫域名指向、服务器设置、域名配置以及反向IP登记等等。说得简单点就是将好记的域名解析成IP，服务由DNS服务器完成，是把域名解析到一个IP地址，然后在此IP地址的主机上将一个子目录与域名绑定。 英文名：DNSR(domain name system resolution) 在域名注册商那里注册了域名之后如何才能看到自己的网站内容，用一个专业术语就叫“域名解析”。在相关术语解释中已经介绍，域名和网址并不是一回事，域名注册好之后，只说明你对这个域名拥有了使用权，如果不进行域名解析，那么这个域名就不能发挥它的作用，经过解析的域名可以用来作为电子邮箱的后缀，也可以用来作为网址访问自己的网站，因此域名投入使用的必备环节是“域名解析”。 域名解析(17张) 我们知道域名是为了方便记忆而专门建立的一套地址转换系统，要访问一台互联网上的服务器，最终还必须通过IP地址来实现，域名解析就是将域名重新转换为IP 地址的过程。一个域名对应一个IP地址，一个IP地址可以对应多个域名；所以多个域名可以同时被解析到一个IP地址。域名解析需要由专门的域名解析服务器(DNS)来完成。解析过程，比如，一个域名为：***.com，是想看到这个现HTTP服务，如果要访问网站，就要进行解析，首先在域名注册商那里通过专门的DNS服务器解析到一个WEB服务器的一个固定IP上：211.214.1.***，然后，通过WEB服务器来接收这个域名，把***.com这个域名映射到这台服务器上。那么，输入***.com这个域名就可以实现访问网站内容了.即实现了域名解析的全过程；人们习惯记忆域名，但机器间互相只认IP地址，域名与IP地址之间是对应的，它们之间的转换工作称为域名解析，域名解析需要由专门的域名解析服务器来完成，整个过程是自动进行的。域名解析协议（DNS）用来把便于人们记忆的主机域名和电子邮件地址映射为计算机易于识别的IP地址。DNS是一种c/s的结构，客户机就是用户用于查找一个名字对应的地址，而服务器通常用于为别人提供查询服务。

易名域名解析教程

设置域名解析？（www和泛解析） 登陆ID后，可以通过“管理中心——用户菜单——域名管理——域名管理——（请输入条件查询信息）——列出所有域名——（找到对应域名）——[管理]——解析管理”进入“域名控制面板”操作设置。 1）登录ID，进入管理中心“用户菜单——域名管理”。 2）在输入条件查询信息中输入关键字，通过“域名类型”“注册模版”“域名分类”“域名状态”等多种方式或选择其中一种后，点击“查询”来查找域名。（注：可以直接点击“查询”列出所有域名）

3）查找到需要解析的域名后，点击域名后的[管理]按钮，即可进行相应操作。 4）在域名管理页面中选择“解析管理”进入域名解析操作界面。

5）按照图示进行设定之后，点击新增一条，即可完成域名解析。 例如域名：https://www.doczj.com/doc/2e499645.html,，主机名设置*（泛解析），类型A，IP地址即为您主机的IP，设置后即可以任何前缀+域名进行访问，如 https://www.doczj.com/doc/2e499645.html,或https://www.doczj.com/doc/2e499645.html,等等；主机名为空（没有填写任何字符），类型A，IP地址即为您主机的IP，设置后是以域名直接访问；如 https://www.doczj.com/doc/2e499645.html, 主机名为www，类型A，IP地址即为您主机的IP，设置后是以www+域名进行访问，如https://www.doczj.com/doc/2e499645.html,。

如何设置别名记录(CNAME)？ 登录ID后，可以通过“管理中心——用户菜单——域名管理——域名管理——（请输入条件查询信息）——列出所有域名——（找到对应域名）——[管理]——解析管理”进入“域名控制面板”操作设置别名记录。 1）登录ID，进入管理中心“用户菜单——域名管理”。

2）在输入条件查询信息中输入关键字，通过“域名类型”“注册模版”“域名分类”“域名状态”等多种方式或选择其中一种后，点击“查询”来查找域名。（注：可以直接点击“查询”列出所有域名） 3）查找到需要设置别名记录的域名后，点击域名后的[管理]按钮，即可进入域名管理页面。

动物运动方式的多样性练习题

第16章第1节动物运动方式的多样性同步练习 一、选择题 1.下列有关动物运动的说法正确的是（） A.动物运动方式的多样性是对不同生活环境的适应，虽然它们的运动器官不同，但它们的共同特点是：具有适应不同环境的特化的运动器官。 B.鸟类在不同的季节里都有迁徙行为。 C.能在空中飞行的动物只有鸟类和昆虫。 D.海蛇、草履虫、野鸭、河蚌的运动方式均是游泳。 2.世界上最大的鸟――鸵鸟的运动方式主要是（） A.飞行 B.爬行 C.游泳 D.行走 3.选出运动方式相同的一组动物（） A.河蟹、野鸭 B.袋鼠、鸵鸟 C.企鹅、麻雀 D.鳄鱼、蜥蜴 4.下列动物的运动方式主要为飞行的是： A 、鸵鸟 B、企鹅 C、蝙蝠 D、蚕蛹 5.动物通过运动提高了适应环境的能力，蜥蜴的主要运动方式为： A 、飞行 B、跳跃 C、爬行 D、奔跑 6.下列哪项不是鸟类迁徙的意义： A、获取足够的食物 B、寻找适宜的生活环境 C、产生有利变异 D、有利完成生殖活动 7.变形虫的运动依靠：

A、纤毛 B、鞭毛 C、伪足 D、伸缩泡 8.生活在亚洲丛林中的鼹鼠在伸展四肢的时候，可以看到其身体两侧皮肤的飞膜，由此可推测鼹鼠的运动方式是： A、滑翔 B、奔跑 C、爬行 D、飞翔 二、非选择题 1.阅读下面短文，回答问题： 一只可爱的小兔子在长满青草和开有小花的山坡上，一边沐浴着灿烂的阳光，一边品尝着清香可口的小草。它不时地移动着身体，走到最嫩的小草旁。突然，可爱的小精灵发现天空中一只老鹰迅速向下飞来，它飞快地向坡那边的林子里跑去。小白兔拼命地奔跑着，越过小沟，老鹰迅速地飞着、追着。小白兔终于躲进了林子，老鹰灰溜溜地飞走了。敌情解除后，小白兔发现林子那边的草长得更茂盛，花开的更艳丽，又自由地在这边草地上快活地舞蹈着。 （1）说出上面短文中描述了动物哪些运动方式_______________________________。 （2）试说出小白兔具有的运动本领对它有哪些意义。__________________________ ________________________________________________。 2.动物的运动因种类而不同，根据常见的类别填写下表：（填写对应序号）

动物的活动方式教案

动物的活动方式教案 【篇一：科学活动：动物的活动方式】 领域活动计划 科学活动：动物的活动方式（动植物） 一、活动目标： 1、发现动物的活动方式是多种多样的。 2、尝试用肢体动作模仿动作的活动方式。 二、活动准备： 经验准备：请家长带幼儿到动物园观察动物。 教材配套：教育挂图《领域活动—科学— 动物的活动方式》，操作材料：《送动物回家》，亲子手册：《领 域活动—谁会跑？》。 三、活动过程： 1、引导幼儿观察挂图，说说挂图上有哪些动物。 引导幼儿观察挂图上动物的活动方式，说说：挂图上的动物都有哪 些活动方式？哪些动物是用脚行走的？哪些动物主要是在天空飞行的？哪些动物是在水里游的？ 2、说说动物都有哪些活动方式。 引导幼儿说说动物都有哪些活动方式？。 教师结合挂图，小结：小鸟有两只脚，能在地上行走，但主要是在 天上飞。河里的小鱼只能在水里游；而鳄鱼也有脚，既可以在水里游，也能在陆地上行走。猴子、老虎、小兔、斑马都是靠脚来行走的。蛇是靠身上的鳞片和地面摩擦来行走的。动物们的活动方式多 种多样。 3、送小动物回家。 引导幼儿完成操作材料《送动物回家》，按照动物的活动方式将贴 纸贴在合适的位置。 4、模仿游戏。 引导幼儿玩模仿动物活动的游戏。 玩法：有而模仿动物活动，边做动作边说：我是xxx，我会飞（跑、走、游、跳）。 5、引导幼儿放松，活动结束。 活动反思：

透过一个故事，遇到危险的长颈鹿，将孩子带进这个活动，长颈鹿在遇到危 险的时候，生活在不同地方的动物纷纷给出意见，以此来了解动物们分别生活的地方并有什么不同。孩子们兴趣很浓厚，特别是故事也很有吸引力。除了个别孩子讲话，大家都积极参与活动，有的积极发言。 【篇二：《动物运动的方式》教学设计(2)】 第一节动物的运动方式 教学过程 【篇三：《动物运动的方式》教学设计(1)】 动物的运动方式 ●教学目标 知识目标 1．列举生物多样性三个层次，概述它们之间的关系。 2．认识我国生物多样性的现状及其独特性。 3．说明保护多样性的重要意义。 能力目标 1．通过对课本资料分析，培养学生思考分析、归纳总结能力，学会收集和整理信息的方法。 2．培养学生在已有知识和生活经验的基础上获取新知识的能力、综合运用知识能力。 情感目标 1．通过本章学习，主要在同学心目中建立起生物（包括人）与环境相适应的辩证观点。 2．激发同学们保护生物栖息环境、保护生态系统的多样性的热情、渗透爱国主义教育。 ●教学重点 1．生物多样性的三个方面内容及它们之间的关系。 2．基因多样性。 3．说明保护多样性的意义。 4．培养学生收集和整理信息的能力。 ●教学难点 1．生物多样性的三个方面内容以及它们之间的关系。 2．基因多样性。

初二生物动物运动方式的多样性教案

初二生物动物运动方式的多样性教案 第16章第1节动物运动方式的多样性 知识与能力目标： 举例说出动物运动方式的多样性;举例说明动物运动的重要性。 过程与方法目标： 通过调查、观察等，收集有关动物运动的资料，培养学生应用资料分析问题、解决问题的能力;通过观察、讨论、交流等进一步培养学生自主学习、合作学习、探究学习的能力。情感态度与价值观目标： 通过合作讨论，培养爱护大自然的情感，建立起生物(包括人)与环境相适应的辩证观点。激发同学们保护生物栖息环境、保护生态系统的多样性的热情。 教学重点： 举例说出动物运动方式的多样性;举例说明动物运动的重要性。 教学难点： 举例说明动物运动的重要性。 课前准备： 学生准备： ①调查和观察生活在水中、空中、地面下、地面上动物的运动方式并记录在表格中以及收集动物运动方式与生活环境

的关系的有关资料。 主要活动区域动物运动方式 天空 陆地 水中 ②收集有关动物运动的图片和画册。 ③准备各种典型的易于捕捉的较小型的动物。 教师准备： 制作多媒体课件;准备观察实验的用的小动物。 教学进程 课堂流程教师活动学生活动 情境导入 多媒体展示视频 去年东南亚发生海啸时，在泰国的普吉岛，有头大象驮着许多的孩子快速奔跑逃离了危险的海滩。那么动物的运动对动物有什么意义呢? 人们常说：海阔凭鱼跃，天高任鸟飞。不同的动物有着不同的运动方式。鹰击长空、鱼翔浅坻、麋鹿奔跑、企鹅游弋等等构成了大自然动态的美丽画卷。 下面我们先来欣赏一段大自然中动物运动的片段。 自然界中各种动物的运动 通过刚才的欣赏你看到了什么?有什么感受? 激起学生的好

奇心和求知欲，激发学习的主动性，提高学习兴趣 感受自然界中动物运动方式的多样性。说出动物在运动并感受大自然的美。

动物运动方式的多样性

动物运动方式的多样性 课题 : 《动物运动方式的多样性》学案时间: 2019年10月20日 主备: 张成班级：八年级（）班学生____________ 【学习目标】 1、举例说出动物运动方式的多样性。 2、举例说明动物运动的重要性。 【自学导航】 自主学习：阅读课文50-51页，完成以下练习。 1、自然环境 2、动物的运动方式多种多样，主要有 3、动物在长期的进化过程中，逐渐形成一系列通过运动能力。 4、动物通过运动能迅速迁移到更为适宜的和，从而有利于自身的和。 【合作探究】 小组展开讨论，交流展示 E.飞行或滑翔 F.旋转式运动 G.翻筋斗运动 H.奔跑探究二：举例说动物运动的重要 性 动物通过运动主动的适应______________ 1. ___________________________________________________________________ 如:___________________________________________________________________ 2. ____________________________________________________________________ 如：__________________________________________________________________ 【分层检测】 一、应知应会 1、下列动物的运动方式主要为飞行的是： A 、鸵鸟 B、企鹅 C、蝙蝠 D、蚕蛹

2、动物通过运动提高了适应环境的能力，蜥蜴的主要运动方式为： A 、飞行 B、跳跃 C、爬行 D、奔跑 3、下列哪项不是鸟类迁徙的意义： A、获取足够的食物 B、寻找适宜的生活环境 C、产生有利变异 D、有利完成生殖活动 二、达标测评 1.世界上最大的鸟――鸵鸟的运动方式主要是------------------------------------------------------------------（） A.飞行 B.爬行 C.游泳 D.行走 2.选出运动方式相同的一组动物-------------------------------------------------------------------------------------（） A.河蟹、野鸭 B.袋鼠、鸵鸟 C.企鹅、麻雀 D.鳄鱼、蜥蜴 3.下列动物的运动方式主要为飞行的是：------------------------------------------------（） A 、鸵鸟 B、企鹅 C、蝙蝠 D、蚕蛹 4.动物通过运动提高了适应环境的能力，蜥蜴的主要运动方式为：--------------------------（） A 、飞行 B、跳跃 C、爬行 D、奔跑 6.下列哪项不是鸟类迁徙的意义：------------------------------------------------------（） A、获取足够的食物 B、寻找适宜的生活环境 C、产生有利变异 D、有利完成生殖活动 7.生活在亚洲丛林中的鼹鼠在伸展四肢的时候，可以看到其身体两侧皮肤的飞膜，由此可推测鼹鼠的运动方式是：------------------------------------------------------------------------------（） A、滑翔 B、奔跑 C、爬行 D、飞翔 三、拓展提升 一些鸟类通过迁徙来度过严寒的冬天，而其他动物是怎样度过冬天的？

苏教版生物-八年级上册-八年级生物 动物运动方式的多样性精品教案

动物运动方式的多样性 知识与能力目标： 举例说出动物运动方式的多样性；举例说明动物运动的重要性。 过程与方法目标： 通过调查、观察等，收集有关动物运动的资料，培养学生应用资料分析问题、解决问题的能力；通过观察、讨论、交流等进一步培养学生自主学习、合作学习、探究学习的能力。情感态度与价值观目标： 通过合作讨论，培养爱护大自然的情感，建立起生物（包括人）与环境相适应的辩证观点。激发同学们保护生物栖息环境、保护生态系统的多样性的热情。 教学重点： 举例说出动物运动方式的多样性；举例说明动物运动的重要性。 教学难点： 举例说明动物运动的重要性。 课前准备： 学生准备： ①调查和观察生活在水中、空中、地面下、地面上动物的运动方式并记录在表格中以及收集动物运动方式与生活环境的关系的有关资料。 主要活动区域动物运动方式 天空 陆地 水中 ②收集有关动物运动的图片和画册。 ③准备各种典型的易于捕捉的较小型的动物。 教师准备： 制作多媒体课件；准备观察实验的用的小动物。 教学进程 课堂流程教师活动学生活动

动物运动方式的多样性 相互交流，探讨体验 多媒体展示图表 描述动物的运动 观察实验小结 大自然真是多姿多彩，令人赏心悦目。草 木荣枯，候鸟去来，蚂蚁搬家，蜻蜓低飞。动 物的运动是大自然最丰富也是最有韵律的动 态语言。 课前请同学们观察和调查了各种动物的 运动并将各种动物及运动方式按照活动区域 填入表格中，另外还请同学们收集动物运动的 图片。下面请同学们四人一组进行交流，①举 例说出各种动物的运动方式，并关注它们生活 的环境。②说出图片中各种动物的运动方式。 动物的运动方式多种多样，由于生活环境 的不同运动方式也不同。经过交流，引导学 生举例说出动物的运动方式有哪些？ 主要活动区域运动方式 空中 陆地 水中 动物的运动让我们感受到了大自然无穷 的魅力。下面我请几个同学描述几种动物的 运动。 自然界中的各种动物的运动都有着自己 的韵律和美感。现在我们亲自观察几种动物 的运动。注意观察动物的运动方式？如何完成 运动的？ 引导学生形象描述几种动物的运动方式 及如何完成运动的。 不同生活环境中的动物运动方式也不同， 课前观察各种动物的运动方式 并记录，将记录的信息交流， 说出图片中各种动物的运动方 式。 按照活动区域，说出动物的运 动方式：空中的有飞行、滑翔； 陆地上的有爬行、奔跑、跳跃、 攀缘、行走等；水中的有游泳、 漂浮等。 用图片和文字描述几种动物的 运动 将各种典型的易于捕捉的较小 型的动物带到学校进行观察探 究。各小组观察小螃蟹和小虾 子的运动。 学生描述动物的运动 描述所观察的现象。

网站域名绑定和域名解析详细讲解

域名绑定和域名解析详解 如何获得IP地址？ 微企点后台系统为您随机分配主机空间IP地址: 上图右侧红色框框里面那串数字就是IP地址，这个IP地址是随机分配的，请以你看到的IP地址为准。 1、什么是域名绑定？ 域名绑定之后并且做完域名解析，浏览者就可以直接通过设置好的域名直接访问了， 例如：在微企点后台中点击“添加域名”，填写.wqdian.，设置之后就可以直接通过该域名访问到您的。 域名绑定在微企点后台完成 2、什么是域名解析？ 域名解析是把域名指向IP，让人们通过注册的域名可以方便地访问到一种服务。IP地址是网络上标识站点的数字地址， 为了方便记忆，采用域名来代替IP地址标识站点地址。域名解析就是域名到IP地址的转换过程。域名的解析工作由DNS服务器完成。 域名解析在域名注册商后台或解析服务后台完成 提示：对于先绑定域名还是先域名解析，并没有定论，但建议先进行域名绑定操作。 3、如何设置解析域名？ 这里以万网、易名、新网、西部数码、时代互联为例。 特别提醒：www和不带www的网址需要分别解析 一、记录类型「A记录」（要将域名指向主机服务商提供的IP地址，请选择「A记录」）

1、万网 1）首先登录，进入会员中心，点击左侧“我的域名”，选择对应域名后方的“解析”，进入域名解析界面 2）域名解析界面，点击“进入高级设置” 3）进入域名解析高级设置界面 第1步：点击“添加解析” 第2步：选择记录类型“A”记录，设置主机记录为所需容. 第3步：填写记录值 (该IP是微企点为您提供的主机空间IP地址，方法请见上方文章顶端)

完成以上3项后，点击“保存”。一般10分钟到两个小时便可以解析完成。最长不超过6个小时。 备注：TTL指各地DNS缓存您域名记录信息的时间，默认为10分钟（600）。 2、易名 1）、首先登录，点击左上角的“用户名”进入管理中心。 2）、点击管理中心左侧的“域名管理”，点击对应域名后的“解析”

动物运动方式的多样性 教案2(苏教版八年级下册)

动物运动方式的多样性教案2(苏教版八年 级下册) 《动物运动方式的多样性》的教学设计 一、教材结构与内容地位简析 《新生物课程标准》课程内容的设定是以“人与生物圈”为主线，精选了十个主题，即①科学探究；②生物与环境；③生物体的结构层次；④生物圈中的绿色植物；⑤生物圈中的人；⑥生物的生殖、发育与遗传；⑦动物的运动和行为；⑧生物的多样性；⑨生物技术；⑩健康的生活。而“动物运动方式的多样性”是新课标的第六大主题《动物的运动和行为》中的具体内容之一，它被编排在苏教版义务教育课程标准实验教科书生物八年级上册的第6单元《动物的运动和行为》的第16章《动物的运动》的首节。介绍了动物运动的各种方式及动物运动的意义。于前一章节学习了遗传和变异的有关知识，因此学生可以理解于自然环境的复杂多变，动物在长期的进化过程中，逐渐形成了独特的运动器官，从而扩大了活动范围，提高了适应环境的能力.同学们对动物的运动方式不会感到陌生，这方面的知识和经验积累应当很丰富，通过本节的学习之后，可以为后面《动物的行为》的学习起到了很好铺垫作用。 二、教学目标

根据上述教材结构与内容分析，考虑到学生已有的认知结构心理特征，我制定了如下教学目标： 1．基础知识目标：举例说出动物运动方式的多样性和动物运动的重要性。 2．能力目标：培养学生的观察能力，收集和处理信息的能力，分析和解决问题的能力以及交流与合作能力。 3．情感目标：通过学生对动物运动方式的了解，使同学们能更加关注自然界的动物，让他们能与之和谐相处，彼此成为永远的朋友，并深刻理解人与自然和谐发展的意义，提高环境保护意识。 三、教学重点、难点 本节是学生学习本章甚至是本单元的基础，动物运动知识对学生认识动物的本质特征非常重要，动物的运动依赖于一定的结构，动物的结构与功能是统一的。所以重点是要能举例说出动物运动方式的多样性。于动物的行为是一种本能的无意识的行为，是自然选择过程中长期进化的结果。因此，要理解动物运动的意义对学生来说是一个难点。 四、教法：在立足于课堂教学同时，要注意引导学生到大自然中去观察动物的运动，这样既满足了学生的好奇心又可激发学生的求知欲，同时培养了学生的观察能力、动手能力，更重要的是教给了学生探究生物世界的方法，同时增强了学生关爱生命、热爱大自然的意识。

[初二理化生]第一节动物运动方式的多样性

[初二理化生]第一节动物运动方式的多样性

第一节动物运动方式的多样性 教材分析 本节内容介绍了动物运动方式的多样性和动物运动的意义。教学内容充分联系了学生的日常生活，但又不局限于日常生活中所见的动物类型。教学内容需要学生充分收集资料并加以合作讨论学习。经过学习，要求学生能掌握探究生物世界的方法，并培养学生热爱大自然，热爱生命的意识。并为后期学习动物运动的生理基础打下坚实的基础。 教学目标 1、知识与能力： 举例说出动物的运动方式的多样性； 举例说明动物运动的重要性。 2、过程与方法： 培养学生的观察能力； 提高学生的分析问题及表达交流的能力。 3、情感、态度与价值观： 通过活动增强学生的热爱大自然和关爱生命的意识。 学情分析 学生在日常生活中已经积累了一定的生物学知识，对常见动物的运动方式也已经比较了解。但是由于学

生个体差异的存在，学生之间知识了解的范围和情况也各自不同。所以充分利用讨论合作学习的方式来做到知识的共享。教师的重点是组织学生分析和整理资料，并注意在教学过程中充分渗透热爱生命，关爱大自然的思想。 课时分配 1课时 教学设计 教学准备 1、教师准备：教师收 集有关动物运动方式 的视频资料，并制作PPT等 2、学生准备：预习本 节课，收集有关动物运 动方式的资料 教学重、难点 重点：举例说出动物的 运 动 方 式 的 多 样 性 。举例说明动物 运 动 的 重 要 性 。 难点：说明动物运动的

重要性。 教学过程 （一）创设情境，走近课堂 师：动物是大家的朋友，我们在语文课中也学到了很多有关动物的成语，大家能不能来举出一些呢？ 生：动如脱兔、呆若木鸡、守株待兔、一丘之貉、狼狈为奸、狐假虎威、莺歌燕舞、龙腾虎跃、鹬蚌相争渔翁得利、螳螂捕蝉黄雀在后、螳臂当车…… 师：这些成语中有很多都反映了动物的运动 方式。你能不能说出来呢？ 生：有鸟在飞，老虎奔跑…… 师：大家说得不错，那么生活在不同环境中的动物在运动方式上又有什么特点呢？ （二）讨论合作，进入课堂 过渡：不同的动物有着不同的运动方式，但是也有的会有一定的相同之处，这是由什么决定的呢？ 首先，请同学们以小组为单位，分别讨论生活在陆地、水中、空中和能够生活在多种环境中动物的运动方式。 1、动物运动方式

域名解析服务器

域名解析服务器，在某些应用程序中如果手工设定合适的DNS服务器IP地址，则可避免程序自动检测，从而提高连接效率。那么如何查看所在地区DNS域名服务器的IP地址呢？答:在Windows XP环境下的查看方法如下 第一步，在开始菜单的“运行”对话框中输入“command”或“cmd”(引号不输入)，打开命令行窗口。 第二步，输入“Ipconfig /all”单击回车键后，屏幕上将显示出IP地址的相关信息。最后的两行，即“DNS Servers”后的内容就是本地DNS服务器的IP地址。 DNS 定义 DNS 是域名系统(Domain Name System) 的缩写，该系统用于命名组织到域层次结构中的计算机和网络服务。DNS 命名用于Internet 等TCP/IP 网络中，通过用户友好的名称查找计算机和服务。当用户在应用程序中输入DNS 名称时，DNS 服务可以将此名称解析为与之相关的其他信息，如IP 地址。因为，你在上网时输入的网址，是通过域名解析系解析找到相对应的IP地址，这样才能上网。其实，域名的最终指向是IP。 在IPV4中IP是由32位二进制数组成的，将这32位二进制数分成4组每组8个二进制数，将这8个二进制数转化成十进制数，就是我们看到的IP地址，其范围是在1～255之间。因为，8个二进制数转化为十进制数的最大范围就是1～255。现在已开始试运行、将来必将代替IPV6中，将以128位二进制数表示一个IP地址。 大家都知道，当我们在上网的时候，通常输入的是如：https://www.doczj.com/doc/2e499645.html,这样子的网址，其实这就是一个域名，而我们计算机网络上的计算机彼此之间只能用IP地址才能相互识别。再如，我们去一WEB服务器中请求一WEB页面，我们可以在浏览器中输入网址或者是相应的IP地址，例如我们要上新浪网，我们可以在IE的地址栏中输入： https://www.doczj.com/doc/2e499645.html,也可输入这样子218.30.66.101 的IP地址，但是这样子的IP地址我们记不住或说是很难记住，所以有了域名的说法，这样的域名会让我们容易的记住。 DNS：Domain Name System 域名管理系统域名是由圆点分开一串单词或缩写组成的，每一个域名都对应一个惟一的IP地址，这一命名的方法或这样管理域名的系统叫做域名管理系统。 DNS：Domain Name Server 域名服务器域名虽然便于人们记忆，但网络中的计算机之间只能互相认识IP地址，它们之间的转换工作称为域名解析（如上面的 https://www.doczj.com/doc/2e499645.html,与218.30.66.101 之间的转换），域名解析需要由专门的域名解析服务器来完成，DNS就是进行域名解析的服务器。 1、什么是DNS？ DNS是指：域名服务器(Domain Name Server)。在Internet上域名与IP地址之间是一一对应的，域名虽然便于人们记忆，但机器之间只能互相认识IP地址，它们之间的转换工作称为域名解析，域名解析需要由专门的域名解析服务器来完成，DNS就是进行域名解析的服务器。 2、为什么要注册DNS，有什么意义？ 申请了DNS后，客户可以自己为域名作解析,或增设子域名.客户申请DNS时，建议客户一次性申请两个。 3、在域名注册机构注册DNS的步骤及其注册的有关规定是什么？ 目前国际域名的DNS必须在国际域名注册商处注册，国内域名的DNS必须在CNNIC 注册。

第十六章_动物的运动方式教案

第十六章动物的运动和行为 第一节动物运动方式的多样性 一、教学目标 1、培养学生的观察能力，收集和处理信息的能力，分析和解决问题的能力以及交流与合作能力。 2、通过学生对动物运动方式的了解，使同学们能更加关注自然界的动物，让他们能与之和谐相处，彼此成为永远的朋友，并深刻理解人与自然和谐发展的意义，提高环境保护意识。 二、重点难点 举例说出动物运动方式的多样性；举例说明动物运动的重要性。 三、教学资源 多媒体 四、教学设计 在开始时，首先激发学生的学习兴趣，利用精美的引人入胜的画面让学生去感悟，去发现。然后通过观察，合作交流等活动，让学生认知动物运动方式的多样性，理解动物运动的意义，增强保护动物的意识。 五、教学过程 【导入新课】 人们常说：“海阔凭鱼跃，天高任鸟飞。”不同的动物有着不同的运动方式。鹰击长空、鱼翔浅坻、麋鹿奔跑、企鹅游弋等等构成了大自然动态的美丽画卷。 下面我们先来欣赏一段大自然中动物运动的片段。 多媒体展示视频 自然界中各种动物的运动 通过刚才的欣赏你看到了什么？有什么感受？ 激起学生的好奇心和求知欲，激发学习的主动性，提高学习兴趣。 感受自然界中动物运动方式的多样性。说出动物在运动并感受大自然的美。 【授课】 一、动物运动方式的多样性 大自然真是多姿多彩，令人赏心悦目。草木荣枯，候鸟去来，蚂蚁搬家，蜻蜓低飞。动物的运动是大自然最丰富也是最有韵律的动态语言。 动物的运动方式多种多样，由于生活环境的不同运动方式也不同。经过交流，引导学生举

例说出动物的运动方式有哪些？（按照活动区域，说出动物的运动方式：空中的有飞行、滑翔；陆地上的有爬行、奔跑、跳跃、攀缘、行走等；水中的有游泳、漂浮等。） 主要活动区域运动方式 空中 陆地 水中 二、描述动物的运动 动物的运动让我们感受到了大自然无穷的魅力，下面请几个同学描述几种动物的运动。 自然界中的各种动物的运动都有着自己的韵律和美感。现在我们亲自观察几种动物的运动。注意观察动物的运动方式？如何完成运动的？ 引导学生形象描述几种动物的运动方式及如何完成运动的。 不同生活环境中的动物运动方式也不同，水中生活的动物主要依靠游泳、漂浮，陆地上的动物可以有行走、爬行、奔跑、跳跃、攀援等多种运动方式，空中生活的动物主要是飞行、滑翔。不同的运动方式不仅适应于不同的生活环境，而且在动物的身体内也有不同的结构与之相适应。 三、多媒体展示视频 让我们来欣赏大自然中丰富多彩的动物运动。请你说出各种动物的运动方式。 引导学生观看视频：狐狸的跳跃、海牛的游泳、水母的漂浮、鱼的游泳、豹子的奔跑、松鼠的攀缘、袋鼠的跳跃、乌贼的游泳、鹦鹉螺的漂浮、蜗牛的爬行、天鹅的水上奔跑等。 四、动物通过运动主动地适应环境 人也是动物，当今大规模的运动会，运动项目不下数十种。例如游泳，便有自由泳、蛙泳、仰泳等，有些显然是模仿动物。人虽然不会飞，但人类用自己的聪明才智发明了飞机、火箭，比任何动物都能远走高飞。 动物的运动对动物的生存有什么意义呢？我们先来欣赏一段视频。 引导学生观看视频：视频一：热带雨林中的蜥蜴在水面上奔跑 视频二：水獭的游泳、滑行、奔跑、行走 视频三：斑马的迁徙 分析讨论： 分析蜥蜴能在水面上奔跑对它有什么意义；

最新动物运动方式的多样性

动物运动方式的多样 性

第一节动物的运动方式的多样性 资料16-1-1 动物三种运动方式的比较 资料16-1-2 各种动物的运动方式 资料16-1-3 鸟儿为什么会飞 资料16-1-4 鸟类的飞行 资料16-1-5 “天空王子”的飞行器——鸟翅 资料16-1-6 鸟的迁徙 资料16-1-7 动物的迁徙 资料16-1-1动物三种运动方式的比较 奔跑、飞行和游泳是动物最常采用的三种运动方式。大小不同的动物，其最小移动能耗速度及其能耗率是不一样的，通过测定各种不同大小的动物在最小移动能耗速度下的能耗率，我们就可以对三种运动方式的能量消耗情况进行比较。动物的游泳、飞行和奔跑三种运动方式在移动相同距离时的能量消耗是不一样的。 游泳是消耗能量最少的一种运动方式，因为动物在水中浮沉几乎不需要消耗能量，主要是依靠浮力调节。其次是飞行，飞行是借助于高速度下的高能耗率来获得其运动效果的。相比之下，在陆地上移动是最消耗能量的一种运动方式。在这里应当注意的是：对于每一种运动方式来说，虽然每克体重的最小移动能耗都随着动物体重的增加而减少，但就每只动物的最小移动能耗总值来

说，却随着动物体重的增加而增加。这其中的道理是很明显的，想必读者都十分清楚。 资料16-1-2 各种动物的运动方式 兽类最大的特点是行走和奔跑： 一般四肢动物的行动规律有这样的方式，以马为例，开始起步时如果是右前足先向前开步，对角线的左足就会跟着向前走，接着是左前足向前走再就是右足跟着向前走，这样就完成一个循环。接着又是另一次右前足向前，左后足跟着向前，左前足向前，右后足跟着，继续循环下去，就形成一个行走的运动。马除了走步外，还有小跑、快跑、奔跑等方式，各种跑的方式都有一定的运动规律的。 青蛙： 青蛙和鱼不一样，它是既能生活在水里，又能生活在陆地上的动物。当它在水中游水时，用长而有蹼的强大后肢划水游泳；当它在陆地上时，用肌肉发达的强大后肢跳跃。 跳跃是青蛙最主要的活动方式，身体结构也朝向适应跳跃的方向发展。青蛙的后肢比前肢长很多，修长的后肢是名副其实的弹簧腿产生往前冲的力量，比较短的前肢则能减轻落地后的冲击力。跳跃的原理如同压扁的弹簧放松之后往外弹跳出去，而后肢的大腿、小腿及足部平常坐叠在一起就具有压扁的弹簧功能。为了跳更远，腰部的肠骨特别延长和相接并形成可动关节，这样子青蛙跳出去以后，身体拉长更有冲力。长而有蹼的后肢也有助于游泳，让他们能够悠游于水陆两种环境。

[初二理化生]第一节动物运动方式的多样性

第一节动物运动方式的多样性 教材分析 本节内容介绍了动物运动方式的多样性和动物运动的意义。教学内容充分联系了学生的日常生活，但又不局限于日常生活中所见的动物类型。教学内容需要学生充分收集资料并加以合作讨论学习。经过学习，要求学生能掌握探究生物世界的方法，并培养学生热爱大自然，热爱生命的意识。并为后期学习动物运动的生理基础打下坚实的基础。 教学目标 1、知识与能力： 举例说出动物的运动方式的多样性； 举例说明动物运动的重要性。 2、过程与方法： 培养学生的观察能力； 提高学生的分析问题及表达交流的能力。 3、情感、态度与价值观： 通过活动增强学生的热爱大自然和关爱生命的意识。 学情分析 学生在日常生活中已经积累了一定的生物学知识，对常见动物的运动方式也已经比较了解。但是由于学生个体差异的存在，学生之间知识了解的范围和情况也各自不同。所以充分利用讨论合作学习的方式来做到知识的共享。教师的重点是组织学生分析和整理资料，并注意在教学过程中充分渗透热爱生命，关爱大自然的思想。 课时分配 1课时 教学设计 教学准备 1、教师准备：教师收集有关动物运动方式的视频资料，并制作PPT等 2、学生准备：预习本节课，收集有关动物运动方式的资料 教学重、难点 重点：举例说出动物的运动方式的多样性。 举例说明动物运动的重要性。 难点：说明动物运动的重要性。 教学过程 （一）创设情境，走近课堂 师：动物是大家的朋友，我们在语文课中也学到了很多有关动物的成语，大家能不能来举出一些呢？ 生：动如脱兔、呆若木鸡、守株待兔、一丘之貉、狼狈为奸、狐假虎威、莺歌燕舞、龙腾虎跃、鹬蚌相争渔翁得利、螳螂捕蝉黄雀在后、螳臂当车…… 师：这些成语中有很多都反映了动物的运动方式。你能不能说出来呢？生：有鸟在飞，老虎奔跑…… 师：大家说得不错，那么生活在不同环境中的动物在运动方式上又有什么特点呢？（二）讨论合作，进入课堂 过渡：不同的动物有着不同的运动方式，但是也有的会有一定的相同之处，这是由什么决定的呢？ 首先，请同学们以小组为单位，分别讨论生活在陆地、水中、空中和能够生活在多种环境中动物的运动方式。 1、动物运动方式的多样性 学生活动：四人为一小组，一个大组讨论生活在一种环境中的动物。在讨论的过程中，尽量说出有代表性的生物，并选出本小组的发言代表。时间为五分钟。 教师活动：在学生讨论的同时，参与各个小组的活动，指导学生

域名的解析过程

域名的解析过程中采用两种查询方式，其中需要注意的事项： 一、主机向本地域名服务器的查询一般都是采用递归查询。 所谓递归查询就是：如果主机所询问的本地域名服务器不知道被查询的域名的IP地址，那么本地域名服务器就以DNS客户的身份，向其它根域名服务器继续发出查询请求报文(即替主机继续查询)，而不是让主机自己进行下一步查询。 因此，递归查询返回的查询结果或者是所要查询的IP地址，或者是报错，表示无法查询到所需的IP地址。 二、本地域名服务器向根域名服务器的查询的迭代查询。 迭代查询的特点：当根域名服务器收到本地域名服务器发出的迭代查询请求报文时，要么给出所要查询的IP地址，要么告诉本地服务器：“你下一步应当向哪一个域名服务器进行查询”。 然后让本地服务器进行后续的查询。根域名服务器通常是把自己知道的顶级域名服务器的IP地址告诉本地域名服务器，让本地域名服务器再向顶级域名服务器查询。

顶级域名服务器在收到本地域名服务器的查询请求后，要么给出所要查询的IP地址，要么告诉本地服务器下一步应当向哪一个权限域名服务器进行查询。 最后，知道了所要解析的IP地址或报错，然后把这个结果返回给发起查询的主机。 三、递归查询和迭代查询的差别 1.下面举一个例子演示整个查询过程： 假定域名为https://www.doczj.com/doc/2e499645.html,的主机想知道另一个主机https://www.doczj.com/doc/2e499645.html,的IP地址。例如，主机https://www.doczj.com/doc/2e499645.html,打算发送邮件给https://www.doczj.com/doc/2e499645.html,。这时就必须知道主机https://www.doczj.com/doc/2e499645.html,的IP地址。下面是上图a的几个查询步骤：

①主机https://www.doczj.com/doc/2e499645.html,先向本地服务器https://www.doczj.com/doc/2e499645.html,进行递归查询。 ②本地服务器采用迭代查询。它先向一个根域名服务器查询。 ③根域名服务器告诉本地服务器，下一次应查询的顶级域名服务器https://www.doczj.com/doc/2e499645.html,的IP地址。 ④本地域名服务器向顶级域名服务器https://www.doczj.com/doc/2e499645.html,进行查询。 ⑤顶级域名服务器https://www.doczj.com/doc/2e499645.html,告诉本地域名服务器，下一步应查询的权限服务器https://www.doczj.com/doc/2e499645.html,的IP地址。 ⑥本地域名服务器向权限域名服务器https://www.doczj.com/doc/2e499645.html,进行查询。 ⑦权限域名服务器https://www.doczj.com/doc/2e499645.html,告诉本地域名服务器，所查询的主机的IP地址。 ⑧本地域名服务器最后把查询结果告诉https://www.doczj.com/doc/2e499645.html,。 2.关于DNS解析的TTL参数： 我们在配置DNS解析的时候，有一个参数常常容易忽略，就是DNS解析的TTL参数，Time To Live。TTL这个参数告诉本地DNS服务器，域名缓存的最长时间。用阿里云解析来举例，阿里云解析默认的TTL是10分钟，10分钟的含义是，本地DNS服务器对于域名的缓存时间是10分钟，10分钟之后，本地DNS服务器就会删除这条记录，删除之后，如果有用户访问这个域名，就要重复一遍上述复杂的流程。

动物的运动教学设计

青岛版小学科学六年级上册 13、《动物的运动》教学设计 张春梅 济宁市洸河路小学

13、《动物的运动》教学设计 一、教学目标： 科学知识目标： 认识水生动物的主要运动方式是游泳，陆生动物的主要运动方式是爬行、行走、跳跃和奔跑，空中生活的动物的主要运动方式是飞行。 科学探究目标： 1、认识物体运动方式的多样性； 2、能说出常见物体的运动方式，观察分析器运动规律； 3、能够准确地比较常见物体运动速度的快慢； 4、分析探究动物的运动对于动物个体和种族的生存具有怎样的重要意义； 5、能用各种感官对物体的运动进行观察，能用图或文字表述；会查阅书刊及其他信息源；能选择自己擅长的方式表述研究过程和结果。 情感、态度和价值观目标： 1、引导学生自觉运用合作与交流的学习方法； 2、培养学生注意观察、善于观察和分析推理的能力； 3、意识到人与自然要和谐相处，愿意合作与交流。 二、教学重难点： 重点：认识不同动物的运动方式的不同特点。 难点：知道动物运动方式具有与其生活环境相适应的特点； 三、教学方法： 讲述、合作探究相结合 四、教学准备： 教师：多种动物的运动图片资料 学生：搜集与动物运动有关的资料。 五、课时安排： 1课时 六、教学过程：

课前谈话: 同学们，你们喜欢小动物吗？你能说一说它们是怎样运动的吗？激发学生去思考、回忆不同动物的各种运动方式。 （一）创设情境，引入新课 1、同学们，老师也很喜欢小动物，课前老师还搜集了一些小动物运动的视频资料，我们一起来看一看。（出示课件：从电视节目《动物世界》下载的视频资料） 2、同学们，在地球上，生活着很多很多的小动物，今天，我们学习《动物的运动》这节课，一起来探讨不同的动物具有的不同运动方式和规律。 （二）小组自行探究 1.陆地动物的运动方式。 师：生活中我们经常见到运动。你曾经见过生活在陆地的动物都有哪些运动方式？ 小组内先自行交流，然后全班汇报。 师：老师也搜集了一部分在陆地上生活的动物的运动方式，我们一起来看大屏幕。（课件出示：孩子们很少见到的陆地动物的运动方式） 这些动物的运动方式有什么共同特点？ 学生讨论后交流，教师小结并板书：爬行、行走、奔跑、跳跃 2. 水中动物的运动方式。 师：你曾经见过的生活在水中的动物都有哪些运动方式？ 生：游泳 教师随机板书：游泳 师：对，但是它们游泳的方式也各不相同。谁能模仿几种鱼类的游泳方式？学生交流 3. 空中飞行动物的运动方式。 师：你又曾经见过生活在空中的动物都有哪些运动方式？ 生：飞行

动物运动方式的多样性

第一节动物的运动方式的多样性 资料16-1-1 动物三种运动方式的比较 资料16-1-2 各种动物的运动方式 资料16-1-3 鸟儿为什么会飞 资料16-1-4 鸟类的飞行 资料16-1-5 “天空王子”的飞行器——鸟翅 资料16-1-6 鸟的迁徙 资料16-1-7 动物的迁徙 资料16-1-1动物三种运动方式的比较 奔跑、飞行和游泳是动物最常采用的三种运动方式。大小不同的动物，其最小移动能耗速度及其能耗率是不一样的，通过测定各种不同大小的动物在最小移动能耗速度下的能耗率，我们就可以对三种运动方式的能量消耗情况进行比较。动物的游泳、飞行和奔跑三种运动方式在移动相同距离时的能量消耗是不一样的。 游泳是消耗能量最少的一种运动方式，因为动物在水中浮沉几乎不需要消耗能量，主要是依靠浮力调节。其次是飞行，飞行是借助于高速度下的高能耗率来获得其运动效果的。相比之下，在陆地上移动是最消耗能量的一种运动方式。在这里应当注意的是：对于每一种运动方式来说，虽然每克体重的最小移动能耗都随着动物体重的增加而减少，但就每只动物的最小移动能耗总值来说，却随着动物体重的增加而增加。这其中的道理是很明显的，想必读者都十分清楚。 资料16-1-2 各种动物的运动方式 兽类最大的特点是行走和奔跑： 一般四肢动物的行动规律有这样的方式，以马为例，开始起步时如果是右前足先向前开步，对角线的左足就会跟着向前走，接着是左前足向前走再就是右

足跟着向前走，这样就完成一个循环。接着又是另一次右前足向前，左后足跟着向前，左前足向前，右后足跟着，继续循环下去，就形成一个行走的运动。马除了走步外，还有小跑、快跑、奔跑等方式，各种跑的方式都有一定的运动规律的。

域名解析过程及原理

域名解析过程及原理 域名解析过程： 第一步：客户机提出域名解析请求,并将该请求发送给本地的域名服务器。 第二步：当本地的域名服务器收到请求后,就先查询本地的缓存,如果有该纪录项,则本地的域名服务器就直接把查询的结果返回。 第三步：如果本地的缓存中没有该纪录,则本地域名服务器就直接把请求发给根域名服务器,然后根域名服务器再返回给本地域名服务器一个所查询域(根的子域)的主域名服务器的地址。 第四步：本地服务器再向上一步返回的域名服务器发送请求,然后接受请求的服务器查询自己的缓存,如果没有该纪录,则返回相关的下级的域名服务器的地址。 第五步：重复第四步,直到找到正确的纪录。 第六步：本地域名服务器把返回的结果保存到缓存,以备下一次使用,同时还将结果返回给客户机。 让我们举一个例子来详细说明解析域名的过程.假设我们的客户机如果想要访问站点：https://www.doczj.com/doc/2e499645.html, ,此客户本地的域名服务器是https://www.doczj.com/doc/2e499645.html, ,一个根域名服务器是https://www.doczj.com/doc/2e499645.html, ,所要访问的网站的域名服务器是https://www.doczj.com/doc/2e499645.html,,域名解析的过程如下所示: (1)客户机发出请求解析域名https://www.doczj.com/doc/2e499645.html,的报文 (2)本地的域名服务器收到请求后,查询本地缓存,假设没有该纪录,则本地域名服务器https://www.doczj.com/doc/2e499645.html,则向根域名服务器https://www.doczj.com/doc/2e499645.html,发出请求解析域名https://www.doczj.com/doc/2e499645.html, (3)根域名服务器https://www.doczj.com/doc/2e499645.html,收到请求后查询本地记录得到如下结果:https://www.doczj.com/doc/2e499645.html, NS https://www.doczj.com/doc/2e499645.html,（表示https://www.doczj.com/doc/2e499645.html,域中的域名服务器为：https://www.doczj.com/doc/2e499645.html,）,同时给出https://www.doczj.com/doc/2e499645.html,的地址,并将结果返回给域名服务器https://www.doczj.com/doc/2e499645.html,。 (4)域名服务器https://www.doczj.com/doc/2e499645.html,收到回应后,再发出请求解析域名https://www.doczj.com/doc/2e499645.html, 的报文。 (5)域名服务器 https://www.doczj.com/doc/2e499645.html,收到请求后,开始查询本地的记录，找到如下一条记录:https://www.doczj.com/doc/2e499645.html, A 211.120.3.12（表示https://www.doczj.com/doc/2e499645.html,域中域名服务器https://www.doczj.com/doc/2e499645.html, 的IP地址为:211.120.3.12）,并将结果返回给客户本地域名服务器https://www.doczj.com/doc/2e499645.html,。